The objective of this study was to describe and evaluate a training intervention for recruiting patients to randomized controlled trials (RCTs), particularly for those anticipated to be difficult for recruitment.t-tests and supplemented with findings from the content analysis of free-text comments.One of three training workshops was offered to surgeons and one to research nurses. Self-confidence in recruitment was measured through questionnaires before and up to 3 months after training; perceived impact of training on practice was assessed after. Data were analyzed using two-sample P-values <0.05)]. Awareness of hidden challenges of recruitment following training was high—surgeons' mean score 8.8  and nurses' 8.4  ; 50% (19/38) of surgeons and 40% (10/25) of nurses reported on a 4-point Likert scale that training had made “a lot” of difference to their RCT discussions. Analysis of free text revealed this was mostly in relation to how to convey equipoise, explain randomization, and manage treatment preferences.Sixty-seven surgeons and 32 nurses attended. Self-confidence scores for all 10 questions increased after training [range of mean scores before 5.1–6.9 and after 6.9–8.2 (scale 0–10, all 95% confidence intervals are above 0 and all Surgeons and research nurses reported increased self-confidence in discussing RCTs with patients, a raised awareness of hidden challenges and a positive impact on recruitment practice following QuinteT RCT Recruitment Training. Training will be made more available and evaluated in relation to recruitment rates and informed consent. Less than 50% of randomized trials have been estimated to meet their recruitment targets Clinician-related factors have been cited as one of the main reasons for low rates of recruitment in RCTs across different care settings (a)self-confidence in recruiting patients to surgical RCTs(b)awareness of key challenges to effective recruitment(c)self-assessed recruitment practice.Previous RCT recruiter training programs have tended to focus on communication of key issues (what to convey and how to convey it) 22.1Surgeons and research nurses who were actively recruiting to an RCT that involved surgery, or those who planned to recruit to one within the next 12 months, were invited to attend one of four training workshops relevant to their profession (three dates were offered to surgeons and one to nurses). RCTs that were known or anticipated to have recruitment difficulties were primarily targeted. The chief investigator of these trials  invited and encouraged recruiters to attend. Workshops were also advertised through the Royal College of Surgeons, the Medical Research Council's Network of Hubs for Trial Methodology Research, and the University of Bristol's Surgical Trials Centre.2.2Three 1-day workshops lasting 5 hours each were held between March and May 2015 and one in May 2016. The workshops were broadly similar, comprising interactive presentations, group exercises, and discussion based around key recruiter challenges for the different health professionals. The aims of the training were to: (1) share skills and evidence-based knowledge, (2) promote awareness and tackling of key recruitment challenges, and (3) enhance self-confidence in recruiting patients to RCTs.2.2.1www.jclinepi.com for the workshop program and key learning points).2.2.2Method of delivery was determined by the organizers' past experiences of recruiter training in individual RCTs Although the content and delivery of the surgeon workshops was essentially the same, we took into account feedback from attendees. The value of the interactive discussions was reported so more time was devoted to this in subsequent workshops. As we were continuing our research into understanding recruitment barriers in specific RCTs alongside this training, we also updated a few quotes highlighting discomfort with recruitment in later workshops.2.3Questionnaires were given to workshop attendees at three time points: immediately prior to, immediately after, and at 1–3 months posttraining. The self-confidence questions were adapted from those developed by Jenkins et al. Attendees were encouraged to comment on their responses if they wished. Questionnaires were anonymous, given the small number of participants and potential for identification, with the exception of those used in the final workshop a year later. Here, participants were allocated a study number to enable identification and contact of nonresponders.2.4t-tests , means, and standard deviations (SDs) were calculated in Stata v14.1. Although participants in the final surgeon workshop were allocated a study ID, data were analyzed as they were for the previous workshops, that is, comparison of group mean scores before and after training. We initially analyzed self-confidence data from the three surgeon workshops individually. Although there were some differences across groups in their baseline scores which may indicate a group effect, the score changes after training were consistently in the same direction and the confidence intervals (CIs) overlap each other. This gave us confidence in the robustness of the findings so we have presented collated findings from all three surgeon workshops for more precise estimates. Free text was analyzed to illustrate and elaborate on the numerical findings using content analysis to systematically categorize and describe the data Two-sample 33.1Ninety-nine participants (67 surgeons and 32 research nurses) attended one of four workshops relevant to their profession. All participants (with two exceptions) completed a questionnaire prior to training, all except one completed one immediately after training, and 72% (48/67) of surgeons and 81% (26/32) of nurses completed a questionnaire at 1- to 3-month follow-up. Around two-thirds of participants provided at least one free-text comment at one time point. Surgeons were involved with (or in a few cases soon to be involved with) over 40 different RCTs evaluating a surgical intervention, 35 for the nurses. The majority of attendees were actively recruiting to at least one RCT that the University of Bristol was involved with. Key recruiters from these trials were present. Those who attended the surgeon workshops were predominately male (73%) aged 30–39, and those at the nurse workshop were primarily female (87%) aged 40–49 years . Just ov3.23.2.1Levels of self-confidence in discussing various aspects of RCT recruitment before training were similar between surgeons and nurses and tended to hover just over midway between not at all confident (0) and very confident (10)  . SurgeonFree-text comments noted by participants prior to training offered insight into reasons for midlevel confidence scores , Box A. 3.2.2P values <0.05) (P < 0.0001); for nurses, this was in relation to obtaining authentic informed consent for randomization from patients who have a deferential attitude toward them .Immediately after the workshops, participants scored high confidence levels in relation to how they might now feel discussing recruitment to RCTs with patients —mean score for surgeons 8.0  and nurses 8.0  (with 10 being very confident). High confidence levels persisted at follow-up after having had a chance to implement training . There ws <0.05) . The greParticipants provided free-text comments that illuminated the increased confidence scores , Box B. 3.3Immediately after training, participants reported feeling highly aware of the challenges of recruitment to RCTs. The mean level of awareness reported by surgeons was 8.8  and 8.4  for nurses (with 10 being “very aware”). Comments supported a high level of awareness , Box C1.When asked at the end of the workshop how much they felt they had learnt, surgeons reported a mean score of 8.0/10  and nurses 6.7/10  (with 10 being highest). At up to 3 months posttraining, participants still viewed the training as useful in their discussions about RCTs with patients, with surgeons scoring higher on this than nurses—surgeons mean score 8.1/10  and nurses mean score 7.0/10 .Analysis of free-text comments revealed that participants specifically valued the presentations and discussions around the challenges of conveying key RCT terminology, including equipoise and randomization, as well as the discussions around terminology, balancing treatment arms and addressing patients' treatment preferences. Having “real” examples from actual RCTs was deemed helpful. Attendees further valued the interactive nature of the workshop, enabling them to share recruitment difficulties and learn from the experiences of others , Box C2.A sizeable proportion of nurses had been recruiting to RCTs for a number of years, and several had received similar training from the QuinteT team previously but in a more condensed and trial-specific format. Some nurses therefore felt that the course was “preaching to the converted” and that it would be better suited to those with less experience and training, thereby explaining their lower scores on how much they considered they had learnt. They did, however, appreciate the importance of re-evaluating their practice, and it offered confirmation that they were doing the “right thing” , Box C3.3.4Immediately after the workshop participants felt the training was likely to make at least some difference to their future discussions of RCTs with patients, with surgeons feeling this difference to be greater than nurses—mean score 8.1  for surgeons and 6.6  for nurses (with 10 being “a lot of difference”). This was demonstrated in practice as at up to 3 months follow-up half of the surgeons reported that the training had made “a lot of difference” to their RCT recruitment discussions with patients . Slightly fewer nurses reported this . Only 2/38 surgeons and 2/25 nurses reported the training as making “no difference”. Participants felt that the workshop had made them aware of their training needs and improved their recruiting skills, offering ideas for different approaches and explanations. Specific reference was made to improvements in discussions with regard to conveying equipoise, explaining randomization, and exploring treatment preferences , Box D.4In this study, we developed training that was tailored to support doctors' and research nurses' identified needs in recruiting patients to challenging RCTs, for example, those with diverse treatment options. The aim of the training was to increase self-confidence in discussing RCTs with potential participants and improve self-assessed recruitment practice. Content focused on raising awareness and encouraging discussion of clear obstacles and hidden emotional and intellectual challenges to recruitment. Following the QuinteT RCT Recruitment Training, surgeons and nurses reported increased self-confidence in discussing RCTs with patients, a raised awareness of hidden challenges and improvements in how they felt they conveyed key RCT concepts and responded to treatment preferences. This study, therefore, supports the use of training workshops for recruiters to equip and educate them in dealing with recruitment into RCTs in surgery. It has implications for the development and implementation of future training for RCT recruiters.Despite the literature highlighting training needs for health professionals in recruiting patients to RCTs A comparison of recruiter training programs to identify what aspects of training are most effective at optimizing recruitment is hampered by variation in the content, delivery, and format of training, in addition to varying measures of effectiveness Participants in the present study most valued training on conveying equipoise, explaining randomization and exploring treatment preferences—aspects that were attributed to the increased confidence scores and impact on practice. We know from previous research that recruiters can struggle with explaining randomization in a way that patients readily understand The impact of training in terms of self-confidence in recruiting patients to RCTs appeared greater on the whole for nurses than surgeons, although the study was not powered to detect differences across the different health professionals. This finding may have been because surgeons had less opportunity than nurses to put this training into practice in the 3-month follow-up period. Implementing new skills and adapting existing ones are likely to require practice. As nurses had more RCT experience at the outset than surgeons, it is also possible that training reaffirmed nurses' confidence in recruiting. Some nurses highlighted this posttraining. A further observation was that surgeons' baseline self-confidence scores regarding patients who are most critical and suspicious of research were relatively low before training. This was repaired to some extent by the training. A key aspect of the training was managing patients' treatment preferences, which can arise from being suspicious of RCTs A key feature of our training intervention is the separate training of doctors and nurses. Previous research has demonstrated that support and training needs for doctors and nurses overlap, but doctors need support to ensure they are committed to the RCT design and comfortable with the eligibility criteria and interventions options, whereas nurses need support in relation to perceived role conflicts and training on approaching eligible patients Our workshop materials were designed with reference to health care professional training initiatives delivered by other groups The main limitation of the study is that we were unable to assess the impact of training on actual practice in terms of recruiting patients and relied instead on self-assessed impact. While this may not be an objective reflection of impact, participants were consistent in their responses—perceiving immediately after training that it was likely to have some impact on how they discussed RCTs with patients and then reporting it as having had an impact at up to 3-month follow-up. Moreover, free-text responses were similar from the different health professionals and highlighted the key training areas—consideration of conveying equipoise, explaining randomization, and exploring treatment preferences. Although response rates at 1- to 3-month follow-up were high (72% for surgeons and 81% for nurses), it is possible that those who did not reply did not experience an impact of training on their recruitment practice or did not recruit patients in this time period. We acknowledge that there may be a degree of selection bias in terms of the types of people who agreed to the training, but personal invites from the chief investigator or trial manager resulted in each RCT having their key recruiters attend. As questionnaires from the majority of attendees were anonymous, we were unable to account for paired responses, which if analyzed using an appropriate regression model might have increased the reliability of the findings. It also meant that we were unable to undertake stratified analysis or explore a group effect. With regard to the latter point, the direction of change of self-confidence scores after training compared with before is consistent for both surgeons and nurses and mirrors the findings from other studies A further limitation is that we developed and evaluated our own training intervention. Findings are preliminary, as training was delivered in the relatively early stages of development, but they are promising and in line with other similar studies The training provided support to those working on a number of diverse RCTs within the field of surgery, as opposed to focusing on the issues inherent within a particular trial. Working within individual RCTs to identify and address specific issues may lead to a greater impact. The QRI, a two-staged process implemented in RCTs deemed difficult for recruitment or those experiencing difficulties, explores where the key difficulties in the trial might be focused. Strategies are then employed to address them (largely involving feedback and training akin to that given within the present study) 5A 1-day training workshop for health professionals that focuses on the emotional and intellectual challenges of recruiting patients to RCTs looks promising in increasing confidence with recruitment, raising awareness of hidden challenges and impacting positively on self-assessed recruitment practice. Although the training intervention evaluated in this study was in the relatively early stages of development, findings are encouraging. The intervention will continue to be developed, made more available, and be evaluated in relation to informed consent and recruitment rates.


The number of functional teats is an important selection criterion in pig breeding. Inherited defects of the udder, such as the inverted teat, do have a considerable negative impact on the nursing ability of the sow. To investigate the genetic background of this defect and the number of functional teats in Swedish maternal lines, samples from 230 Yorkshire pigs were selected for genotyping using the PorcineSNP60K BeadChip (Illumina Inc.), each pig with at least one inverted teat was matched with one non-affected pig (fullsib or pairs with matching herd and gender). A genome-wide association study on these 230 pigs was performed using the two-step approach implemented in GenABEL using 46,652 single nucleotide polymorphisms across all autosomes and the X chromosome. A number of significant regions were identified for the inverted teat defect on chromosomes 2, 10, and 18. Many of the regions associated with the number of functional teats were located in the same or close regions, except two associated markers on the X chromosome and one on chromosome 3. We identified some of the regions on chromosomes previously reported in one linkage and one gene expression study. We conclude, despite being able to suggest new candidate genes, that further studies are needed to better understand the biologic background of the teat development. Despite the in-depth comparison of identified regions for the inverted teat defect done here, more studies are required to allow a clear identification of genetic regions relevant for this defect across many pig populations.The online version of this article (doi:10.1007/s13353-016-0382-1) contains supplementary material, which is available to authorized users. The successful breeding for increased litter sizes has made the number of functional teats a critical trait for selection for increased litter size in pigs. If the number of teats is lower than the number of piglets, the piglet mortality might be increased. Cross-fostering might to some degree even out the lack of a functional teat for each piglet. Nevertheless, the selection pressure on breeding sows with a healthy udder and many functional teats is high. A common type of inherited non-functional teat is the inverted teat. It has been shown that, while some inverted teats protrude just before or during the lactation, those that remain inverted cannot be suckled by the piglet  and its receptor (PTHR1), which have been suggested as possible regulators for inverted teats  teat, and if possible samples from one full sibling with no inverted teat (control). A total of 230 animals from seven different nucleus herds were included in the study. The samples from 100 full sib pairs and 15 unrelated pairs were collected between 2008 and 2012. Phenotypic records used in the analyses were total number of inverted teats, number of functional teats, and total number of teats, recorded at approximately 5 months of age.www.nordicgenetics.com) scored, as part of the performance testing, young pigs (at approximately 100 kg and 5 months of age) for the total number of teats (left + right side), number of functional teats, and number of non-functional teats (including inverted teats). Pedigree, phenotypic, and management (technician identity) information were stored in the Nordic Genetics database. Additional information from the database including pedigree, herd, and date of birth, as well as the technician scoring the animals, were used for the further analysis. More details on the population and scoring as well as results of heritability estimations can be found in a previous publication . The DNA concentration was measured using a Nano Drop 8000 spectrophotometer (Thermo Scientific). The samples were genotyped with the Illumina PorcineSNP60K BeadChip  containing 61,565 SNPs. Quality control was performed; markers and samples were removed due to low call rate (<95%) and minor allele frequency (<5%). A total of 23% of data were excluded prior to the association analysis. and 46,652 markers remained for the association analysis.Blood samples were collected in EDTA tubes from the jugular vein of each pig in connection with regular performance testing in the nucleus herds when the pigs weighed approximate 100 kg. DNA was extracted from blood samples of 230 selected pigs using the QIAGEN QIAsymphonyhttp://CRAN.R-project.org) using the GRAMMAR–gamma method  distance clustering. To identify population stratification in the sample population, quantile–quantile plots (QQ plots) were used. The genome-wide association analysis was performed in R  to identify potential candidate genes. Positions and other details of previously reported QTL were downloaded from the information on the PigQTLdb  in February 2016 . Haplotypes were built from the SNP data of the current study and allele frequencies compared to those of the microsatellite markers. A family-based association test was performed to identify associated regions in common between both studies and in a combined dataset  . Selection for the total number of teats would therefore not necessarily increase the incidence of inverted teats; however, an early study reported a low but negative genetic correlation between the number of inverted teats and total number of teats Hittel . Also thLittle is known on the development of the inverted teat defect. There are only relatively few recent reports on incidences in commercial sow lines. The problems of an early determination of non-functional teats in pigs  of 0.65 Fig.  for the p-value for number of inverted teats and number of functional teats, as well as additional suggestive SNPs in the same regions on chromosomes 2, 10, and 18 are shown in Table We identified regions on the genome showing significant and suggestive association with the total number of inverted teats, the number of functional teats and the total number of teats Figs. , and 3a.http://www.animalgenome.org/repository/pig/Genome_10.2_mappings/SNP_pos_build10.2_final.txt). Results of the combined assembly are shown in The identified loci associated with the number of inverted and functional teats were distinct from those identified for the total number of teats. QTLs were identified on chromosome 6, 8, 13, and 14 Table , 2016. EDespite being able to identify associated markers, there was a lack of strong associations: one of the suggestions is that animals from the Yorkshire population have been highly selected for maternal traits, including number of teats. The observed phenotypes did verify this, as no animal with an extreme high or low number of teats was found. A QTL verification study using commercial sow lines did identify similar difficulties when comparing results from a cross between extreme breeds and such from commercial maternal lines  on chromosome 1, the parathyroid hormone-like hormone gene (PTHLH) on chromosome 5, and the transforming growth factor beta 1 gene (TGFB1) gene on chromosome 6 in an experimental population  on chromosome 1 was found to be highly expressed in inverted teats, while the growth differentiation factor 8 (GDF8) on chromosome 15 was highly expressed in normal teats. The differential expression of the epidermal growth factor (EGF) on chromosome 8 and its receptor (EGFR) on chromosome 9 did not align to the significant regions in our study (Yammuen-Art GPCR135) was more highly expressed in teats from affected sows than in normal sows  on chromosome 2 as a potential candidate gene, and it was found to be more highly expressed in teats from affected sows than in normal sows, but this gene was not associated with the inverted teat defect (Yammuen-Art IGF-II), was also found to be highly expressed in inverted teats in the same study (Yammuen-Art FGF1), which is supposed to be involved in promoting mammary ductal development during sexual maturity in mice (Akers SPRY4) gene (Table SPRY4 is an inhibitor of the mitogen-activated protein kinase (MAPK) and suppresses the insulin receptor. However, there is no clear suggestion on how this impacts the development of the teat. Other genes previously suggested as potential candidate genes for the inverted teat defect on chromosome 2 are the follicle-stimulating hormone (FSH) and peptidylprolyl cis/trans isomerase (PIN1)  Jonas ; howeverhttp://www.animalgenome.org/repository/pig/Genome_10.2_mappings/SNP_pos_build10.2_final.txt) repository from the build 10.2. The gene aquaporin 3 (AQP3) is a potential candidate gene on this chromosome as it is a glycerol transporter in the mammalian skin  are located 13 kb from each other and about 60 kb from the carboxypeptidase vitellogenic-like (CPVL) gene on chromosome 18. The protein encoded by this gene is a carboxypeptidase; however, the exact function of this protein has not been determined.We identified on chromosome 18, together with chromosome 2, the markers most associated with the number of inverted and functional teats. The markers with the lowest FGF2) on chromosome 14. The signalling of the FGF family is relevant for mammary gland development, and it has been shown that FGF2 controls the ductal elongation  on the X chromosome had previously been proposed as a potential candidate gene for the inverted teat defect  gene is located closely to the associated marker on chromosome 6, but we could not find a study clearly suggesting its role in the development of the mammary tissue. The other interesting chromosome for the total number of teats is chromosome 8, where Bidanel et al.  from pigs with and without inverted teats did not lead to any conclusive knowledge of the genetic control of this inherited defect Below is the link to the electronic supplementary material.


There are numerous applications that use the structures of protein-ligand complexes from the PDB, such as 3D pharmacophore identification, virtual screening, and fragment-based drug design. The structures underlying these applications are potentially much more informative if they contain biologically relevant bound ligands, with high similarity to the cognate ligands. We present a study of ligand-enzyme complexes that compares the similarity of bound and cognate ligands, enabling the best matches to be identified. We calculate the molecular similarity scores using a method called PARITY , which can conveniently be combined to give a similarity score for all cognate reactants or products in the reaction. Thus, we generate a rank-ordered list of related PDB structures, according to the biological similarity of the ligands bound in the structures.  •We present PARITY, matching atoms in identical topology to gauge ligand similarity•Bound-cognate ligand similarity is a useful metric for ranking PDB structures•Only 26% of enzyme structures in the PDB have bound-cognate ligand similarity ≥0.7•We provide rank-ordered lists of PDBs with the most biologically relevant ligands Tyzack et al. present a similarity study, comparing bound ligands in enzyme structures deposited in the PDB with biological substrates in native reactions. They identify structures most likely to contain relevant information regarding enzyme-substrate interactions and binding interfaces. With unmodified target proteins, it is often not possible to bind cognate ligands without the reaction occurring, so compounds with varying degrees of similarity to the cognate ligands are used as surrogates in co-crystallization experiments.Enzymes are an important group of drug targets where understanding ligand-enzyme binding requires inspection of crystal structures with bound ligands. The ligand-enzyme complex becomes more informative if the bound ligand is similar to the cognate ligand  to compare the similarity of bound and cognate ligands and automatically annotate the current content of the PDB. The methodology is described in detail in the online In this section, we present the analyses of the similarity of bound ligands to cognate ligands across the dataset . SeparatA key observation is the high level of PDB structures where there are no bound ligands or that have bound ligands with low similarity to cognate ligands. It can be seen from Selecting the most similar PDB-KEGG match for each cluster of 100% sequence identity improves the situation since many of the poorer matches can be discarded. This improves further by selecting the most similar PDB-KEGG match for each KEGG and EC, where the proportion with a similarity score greater than or equal to 0.7 increases to 58.9% and 62.9%, respectively. This emphasizes the usefulness of the similarity measure and how it can be used to identify PDB structures with bound ligands most similar to the cognate ligands.To further demonstrate the value of this dataset, we provide two use cases where it is required to find PDB structures with the most similar bound ligands, first for a particular KEGG reaction and second for a particular EC reaction. In these examples, we are able to identify the best matches from a large number of structures with varying degrees of similarity, eliminating the need to manually sift through the data and enabling research effort to be focused on inspecting the ligand binding or other higher value activities.2HA4 , is shown in The number of PDBs referencing R01026 (acetylcholine acetylhydrolase) via its parent EC references 3.1.1.7 (acetylcholinesterase) and 3.1.1.8 (cholinesterase) is 840, with the frequency distribution of binned similarity scores shown in The number of PDBs referencing EC 4.2.1.75 (uroporphyrinogen III synthase) is 59, with the frequency distribution of binned similarity scores shown in A limitation of the methodology described is the potentially imprecise matching of PDB to KEGG via the EC reference number(s) in SIFTS. In many cases a PDB is uniquely matched to one KEGG reaction via its sole EC reference (38.8% of our dataset are uniquely mapped to one KEGG reaction in this way), but in some cases multiple EC numbers are listed, the EC number is partial, or the EC number maps to multiple KEGG reactions. In these cases, it is not possible to know precisely which KEGG reaction(s) is the “correct” match for that structure; some KEGG reactions may be catalyzed by the enzyme, but with lower efficiency, and some may not occur at all. This is a problem we do not attempt to resolve here; rather we explicitly show in the output data the number of KEGG reactions associated with each PDB structure so it is obvious to the user how many KEGG reactions have been matched, from which one will be selected.It is interesting to note that 83.2% of the full PDB dataset map to only one EC number and so any related KEGG reactions can be expected to share similar chemistry on similar substrates. In addition, a further 10.2% have multiple EC numbers, but all share the same EC subsubclass  and so can be expected to share similar chemistry but on more different substrates. This leaves a further 6.6% that map to multiple EC numbers that differ more fundamentally at the EC class or subclass level. It would make an interesting study to measure and compare the similarity of KEGG reactions mapped to PDB structures via EC numbers or within EC subsubclasses, but this is not considered further here, where the focus remains on the similarity of bound and cognate ligands.The purpose of this work is not to attempt to assign function to structure based on bound ligands, rather it is intended to prioritize searches from a KEGG or EC perspective to those structures most likely to contain relevant bound ligands. Therefore, this does not eliminate the need for a user to verify that a particular PDB structure can indeed perform the query reaction. Bearing this in mind, it is still informative to compare some of the PDB to KEGG matches above a predefined similarity threshold to manually curated data to provide some insight into the relevance of the best matches.In order to test the impact of this uncertainty, we have examined a manually curated set of PDBs from the M-CSA . The M-COur results show that there is high variability in the similarity of bound ligands to cognate ligands across PDB structures, with a high proportion of PDB structures containing no bound ligands or containing ligands with low similarity to the cognate molecule. These results emphasize the importance of a measure of the relevance of the PDB in terms of the similarity of bound and cognate ligands (in addition to other more established metrics such as resolution) to help researchers more easily identify the most biologically relevant PDB structures as starting points for further study.This variability in similarity of bound and cognate ligands makes some PDB structures much more useful and informative for the diverse virtual screening methods described in the The similarity measure described in this paper finds the proportion of atoms residing in identical topology between molecules, a method we call PARITY. We favor this similarity metric since it avoids the situation where a small change to a crucial connecting atom disrupts the maximum common substructure or molecular paths, having a disproportionately large detrimental effect on the similarity score. It also enables the ligand similarity scores to be conveniently combined into a KEGG reactants/products similarity score favoring more complete matching of reactants of products.Where there is sufficient similarity between bound and cognate ligands there is potential to use the bound atom coordinates in the matched region from PARITY to constrain the docking of the cognate ligand. The docked cognate-ligand enzyme structures obtained would have the potential to enhance and supplement virtual screening datasets and facilitate the further study of ligand-enzyme interactions. We aim to generate a dataset of docked cognate-ligand enzyme structures to be released alongside the similarity scores in an online database.tyzack@ebi.ac.uk).Further information and requests for resources should be directed to and will be fulfilled by the Lead Contact, Jonathan Tyzack  is presented in Firstly, it was necessary to identify enzyme structures within the PDB to form the basis for the similarity study. SIFTS   is a resTo be able to carry out the similarity analysis it was necessary to obtain molecular structures for the bound and cognate ligands. Chemical structures for bound PDB ligands were obtained by matching the ligand reference to a database of SMILES strings provided by the PDB (components.cif), but structures for cognate reactants/products are not always uniquely assigned at the EC level. Therefore, the EC references were mapped to KEGG reactions in the KEGG database , enablinOf the 68,236 PDB structures listed in the SIFTS download, 56,994 could be matched to a KEGG reaction with molecular structures for the reactants/products. Of the remaining 11,242 structures the majority act on polymer substrates: 6,606 belong to EC 3.4 (peptidases); 1,698 belong to EC 3.1 (acting on ester bonds); 1050 belong to EC 3.2 (glycosylases); 838 belong to EC 2.3 acyltransferases; and 1,050 belong to other EC categories. These were excluded from the analysis as similarity calculations are not possible without cognate ligand structures.KEGG has some generic reactions that contain a Markush structure where the R group represents a position where variation is tolerated and often elucidated in more specific child reactions. The similarity methods described in the next section allow for Markush structures by replacing the R group in the cognate ligand with the matched fragment in the bound ligand, allowing comparable similarity scores to be generated for reactions containing Markush structures.I represents the intersection of B and C, U represents the union of B and C, NB is the number of atoms in bound ligand B, NC is the number of atoms in cognate ligand C and Nsim is the number of atoms of the same type in equivalent positons in B and C. The ligand similarity score was binned by rounding to the nearest 0.05 to allow plots of molecule similarity against frequency to be made.The RDKit cheminformatics toolkit  was usedThe advantage of this method over simply using the size of the MCS or path based fingerprint methods is the situation where a small change in the center of a molecule disrupts the MCS and causes a disproportionately large negative change in the similarity score. The PARITY method gives a more intuitive and gradual fall in similarity in this situation, as demonstrated by the comparison between C00026 (2-oxoglutarate) and OGA  in For each PDB to KEGG comparison, ligand similarity calculations are carried out on an all by all basis comparing all bound ligands to all cognate reactants (i.e. compounds on the left-hand side of the KEGG reaction) and to all cognate products (i.e. compounds on the right-hand side of the KEGG reaction), taking the best match to either reactants or products. Whether the match has been made to either KEGG reactants or products is recorded and explicitly documented in the output data.To ensure complete matching of the cognate reaction, it is important to note that all cognate reactants/products must be matched to a bound ligand, and any remaining unmatched cognate molecules will appear as unmatched in the final calculation and be fully reflected in the final similarity score. However, due to the presence of water as a solvent in PDB structures and the difficulty of resolving the positions of hydrogen atoms, compounds C00001 (water), C00080 (proton) and C00282 (dihydrogen) were excluded from the cognate ligands. The cognate ligands are matched to their most similar bound ligand using a greedy matching algorithm, i.e. the next most similar pair of unmatched cognate and bound ligands is always matched.Cm=1→M on one side of a KEGG reaction, and N bound molecules Bn=1→N in the PDB, ligand similarity scores are calculated as follows:B0 as follows:From the resulting list of matched cognate and bound ligands the similarity scores can conveniently be combined to generate a similarity score on a KEGG reactants/products basis. For example, if there are M cognate molecules The similarity scores can then be combined to give a reaction similarity score using:3AVR ) and KEGG R05493  in The methodology to calculate similarity at the KEGG reactants/products level is demonstrated for PDB Once the similarity scores have been calculated the PDB-KEGG comparisons were rank ordered to identify the PDB structures containing the most representative ligands for each EC reference and each KEGG reaction. A Mendeley data repository contains 2 files (ec_pdb_sep17.csv and kegg_pdb_sep17.csv) with the top ranked matches for each EC and KEGG respectively, along with any other PDB-KEGG matches with a similarity score greater than or equal to 0.70. A breakdown of each PDB-KEGG match by pairs of matched bound-cognate ligands is given in the final column. However, to facilitate easy searching by KEGG compound we include an additional file (keggCpd_pdb_sep17.csv) showing for each KEGG compound in each KEGG reaction, rank ordered matches to PDB files where similarity to a bound ligand is greater than or equal to 0.7.The content of the PDB reflects the areas of focus for structural biologists and contains many duplicate structures and homologues, so PDBs were clustered based on identical Fasta sequence to enable the best PDB structure to be identified for each cluster.https://data.mendeley.com/datasets/7c48npgyr8:The csv files described in Rank ordered similarity for each KEGG and EC in https://doi.org/10.17632/7c48npgyr8.2#file-d8c87e4a-76b7-4763-bd7d-61a6aa854021ec_pdb_sep17.csv https://doi.org/10.17632/7c48npgyr8.2#file-c3650d9e-36b8-4126-9c57-aa77a5ce92edkegg_pdb_sep17.csv https://doi.org/10.17632/7c48npgyr8.2#file-8cd8d174-7b52-4e22-9898-d518c626c798keggCpd_pdb_sep17.csv


Patient-reported outcome (PRO) instruments are important tools for monitoring disease activity and response to treatment in clinical trials and clinical practice. In recent years, there have been movements away from traditional pen-and-paper PROs towards electronic administration. When using electronic PROs (ePROs), evidence that respondents complete ePROs in a similar way to their paper counterparts provides assurance that the two modes of administration are comparable or equivalent. The European Organisation for Research and Treatment of Cancer (EORTC) Quality of Life Questionnaire-Core 30 item (EORTC QLQ-C30) and associated disease-specific modules are among the most widely used PROs in oncology. Although studies have evaluated the comparability and equivalence of electronic and original paper versions of the EORTC QLQ-C30, no such studies have been conducted to date for the head and neck cancer specific module (EORTC QLQ-H&N35).This study aimed to qualitatively assess the comparability of paper and electronic versions of the EORTC QLQ-H&N35.Ten head and neck cancer patients in the United States underwent structured cognitive debriefing and usability interviews. An open randomized crossover design was used in which participants completed the two modes of administration allocated in a randomized order. Using a “think-aloud” process, participants were asked to speak their thoughts aloud while completing the EORTC QLQ-H&N35. They were thoroughly debriefed on their responses to determine consistency in interpretation and cognitive process when completing the instrument in both paper and electronic format.Participants reported that the EORTC QLQ-H&N35 demonstrated excellent qualitative comparability between modes of administration. The proportion of noncomparable responses  observed in the study was low . Evidence of noncomparability was observed for 9 of the 35 items of the EORTC QLQ-H&N35 and in no more than 2 participants per item. In addition, there were no apparent differences in level of comparability between individual participants or between modes of administration.Mode of administration does not affect participants’ response to, or interpretation of, items in the EORTC QLQ-H&N35. The findings from this study add to the existing evidence supporting the use of electronic versions of the EORTC instruments when migrated to electronic platforms according to best practice guidelines. Patient-reported outcomes (PROs) are used in clinical trials and clinical practice to assess symptoms, impacts, and health-related quality of life (HRQoL) from the patient perspective. Understanding patients’ symptoms and physical functioning is important in oncology and other disease areas to assess disease activity and response to treatment, and this information best comes from the patients themselves . The EurTumor-specific questionnaire modules supplement the EORTC QLQ-C30, including the EORTC Quality of Life Head and Neck 35-item questionnaire (EORTC QLQ-H&N35). Head and neck cancer is the sixth most common cancer worldwide, with an incidence of over 600,000 newly diagnosed cases each year . The disA number of meta-analyses and systematic reviews of studies evaluating measurement equivalence between ePROs and their validated paper-based equivalents in a number of disease areas have been conducted -17. FindThis study aimed to provide evidence on the qualitative comparability of data collected from paper versus electronic (tablet-based) administration of the EORTC QLQ-H&N35. The primary objective was to explore whether there were any features of the electronic-version of the EORTC QLQ-H&N35 where participants’ understanding and interpretation of instructions, items, and response options differed when compared to the original pen-and-paper version of the instrument. While the primary objective relates to the EORTC QLQ-H&N35, participants also completed the EORTC QLQ-C30 in line with developer guidelines. Feedback from participants regarding the usability of electronic device for completion of the instruments was also investigated.The level of evidence required to assess comparability across modes of administration depends on the extent to which the instrument has been modified from its original format in migration to the new format ,13. MoviThe EORTC QLQ-H&N35 is designed to be administered alongside the EORTC QLQ-C30. This study implemented an open randomized crossover design in which participants completed the two modes of administration for the EORTC QLQ-C30 and H&N35  allocated in a randomized order . The desThis study was approved by Copernicus Group, a centralized Institutional Review Board in the United States (IRB #ADE1-15-702). The study was performed in accordance with the ethical standards in the 1964 Declaration of Helsinki and its later amendments.Ten people with head and neck cancer were recruited to participate into this study in the United States. This sample size is in accordance with recommendations from the International Society for Pharmacoeconomics and Outcomes Research ePRO Good Research Practices Task Force Report, which recommends 5-10 participants for studies involving cognitive debriefing and usability testing where minor modifications have been made in migration to another mode of administration .Participants were required to meet predefined inclusion and exclusion criteria . ParticiThe electronic tablets used in this study  were provided by a third party agency, CRF Health. The device used was an ACER ASPIRE SWITCH 10, with a 10.1-inch display. As the devices used were “dummy devices,” participant responses were not recorded on the devices themselves but participants were asked to read aloud their responses, with responses recorded by the interviewer and documented on the audio-recording of the interview. The device displayed approximately 6 items per screen. Of note, this format differs from the paper versions of the EORTC QLQ-H&N35, which displays approximately 18 items per page.All interviews were conducted by trained experienced interviewers using a semi-structured interview guide. In the absence of existing published evidence regarding the comparability of paper and electronic versions, the EORTC QLQ-H&N35 was the primary focus of discussions, although participants also provided feedback on the interpretation of individual items of the EORTC QLQ-C30.Participants completed the questionnaires on the first mode of administration as part of a “think-aloud” exercise, whereby they were asked to speak aloud their thoughts as they read each instruction and complete each item. Interviewers used focused probes during this process to ensure that the EORTC QLQ-H&N35 was debriefed in full and that participant understanding/interpretation of items and reasons for selecting certain responses was fully understood. Participants then repeated the think-aloud exercise for the second mode of administration. Any apparent differences in interpretation of instrument instructions, items, and response options between modes of administration were explored.Finally, participants were asked for their feedback on the usability of the electronic tablet, any perceived differences in their experience of completing the instruments across modalities, and their preference for either modality.All interviews were audio-taped and transcribed verbatim to allow for qualitative analysis using ATLAS.ti software. All transcripts were assigned a unique patient identification code, which was made up of the interview location number, participant number, participant gender, participant age, and group number . In accordance with the principles of thematic analysis, a coding scheme was developed in which excerpts from transcripts were assigned codes and grouped according to consistent themes. Coding was completed by one researcher.A primary focus of the analysis was to determine the extent to which participant responses to PRO items in electronic and pen-and-paper formats could be considered comparable. In this context, responses were defined as comparable if it was clear from qualitative feedback that the participant had interpreted the item and selected their response using the same thought process for both modes of administration. Crucially, while in many cases participants may have selected identical responses to items for each respective administration, this was not necessary for the formats to be considered comparable. Similarly, even if respondents had selected identical responses, if it was clear from discussion and feedback that the thought process for selecting responses was different then this was highlighted as noncomparable. Where it was not clear whether the participant had interpreted the item differently between modalities, this was counted as not clear. Comparability of response was evaluated for each participant by 2 independent researchers and was checked by the project leader.As the EORTC QLQ-H&N35 is designed to be administered following the EORTC QLQ-C30, numbering of EORTC QLQ-H&N35 items starts at 31. Overall, the EORTC QLQ-H&N35 demonstrated strong evidence of qualitative comparability between modes of administration. The majority of items showed comparability when completed on paper and electronically, with feedback from participants indicating that they had interpreted the item in the same way or used the same thought process when completing the item in both modalities . For exa(01-03-F-50-G1): Item 31. Have you had pain in your mouth?Have you had pain in your mouth? I’m going to say a little only because I have dentures and I think, um, that all has a little bit to do with the, the radiation and stuff. I had a hard time wearing them... I’m going to say I had a little bit of discomfort.PaperUm, have you had pain in your mouth? I’m going to put a little. Again, that’s just the denture thing.ePRO(03-03-F-48-G2): Item 51. Have you had trouble eating in front of other people?A little bit, yes. I feel a little self-conscious sometimes… I was a little more self-conscious about eating in front of others out in public… because of the partial paralysis I have at the corner of my mouth and it just sort of makes me feel a little awkward.ePROI would choose number two, a little bit. More of a self-conscious thing, uh, than it was mechanical.PaperIn total, the proportion of noncomparable responses  observed in the study was low . Evidence of noncomparability was observed for only 9 of the 35 items of the EORTC QLQ-H&N35. For these 9 items, noncomparable responses were typically observed only for a single participant (7/9 items) and never more than 2 individual participants (2/9 items). In instances where responses were noncomparable, participants seemed to use a different thought process to select a response , although participants often selected the same or adjacent response options. Indeed, no instances where response options selected by participants differed by more than one response category between modes of administration were observed (examples provided below). For items 52  and 54 , where 2 individual participants provided noncomparable responses, the different thought processes used may be attributed to participants’ understanding of item wording:(03-03-F-48-G2): Item 40. Have you had problems opening your mouth wide?Have you had problems opening your mouth wide? Uh, quite a bit, number three. Um, biting into sandwiches and, you know, taking spoonfuls of food has been problematic this week.PaperHave you had problems opening your mouth wide? I’m going to go with four, uh, very much ‘cause I’m always having problems opening my mouth wide… I think I kind of consider that similar to the have you had pain in your jaw. And, um, those two kind of are related. Uh, opening my jaw wide, opening my mouth wide is painful.ePRO(03-02-F-39-G1): Item 52. Have you had trouble enjoying your meals?Have you had trouble enjoying your meals? I just say a little, only because of the swallowing being little inconvenient, but other than that I really don’t have any problems.PaperHave you had trouble enjoying your meals? No, not at all. I enjoy my meals.ePRO(01-01-M-59-G1): Item 55. Have you had trouble having social contact with your family?Have you had trouble having social contact with your family? Not at all. We get together for all the family functions, birthdays, and Christmas and Easter and all that good stuff.ePROHave I had trouble having social contact with my family? I don’t know if this pertains to your survey, but because I am living with my mother right now she is really hard of hearing. And between my voice and her ears I don’t talk very much... I’m going to put a little.PaperThere were a small number of instances across 17 items where it was not possible to determine whether participant interpretation was comparable . In the majority of cases, this was due to the brevity of information provided by participants during the think-aloud and subsequent discussion. For example:(03-02-F-39-G1): Item 33. Have you had soreness in your mouth?Have you had soreness in your mouth? Uh, number two, a little… just an achy type pain, and I do have that achiness from time to time.PaperHave you had soreness in your mouth? One, not at all.ePROExploring noncomparable responses in more detail revealed that that these came from 6/10 participants. Among these participants, no individual appeared to interpret items differently or use a different thought process to select a response on any more than two items . DiscernWhen asked directly, most participants (n=8) reported that mode of administration (paper or electronic) made no difference in their understanding of the EORTC QLQ-H&N35 instructions and items or the way in which they selected responses to items. Two participants reported that the mode of administration did influence their ability to understand, interpret, and respond to EORTC QLQ-H&N35 items. One participant commented that the tablet version of the instrument was easier to understand: “where in the paper I may have gone through it quicker.” Another participant reported that it “feels differently looking at it on paper.” While no further information was provided by these participants, their responses were largely comparable across modes of administration . While not the purpose of the current study, equivalence of scores on the EORTC QLQ-C30 was also observed.All 10 participants reported that the tablet device was easy to use, with 7 participants spontaneously adding that it was easier than the paper version. No participants demonstrated any issues with selecting a response on the touchscreen or moving on to the next page of the questionnaire. Similarly, participants did not report any concerns about changing an answer, moving to a previous page, or saving responses. Some participants commented that completing the instrument on the electronic tablet was faster than on paper (n=3) or that there was no difference in completion time between paper and electronic tablet version (n=3).When asked, most participants (n=7) said that they preferred completing the instrument on the electronic tablet than on pen and paper, as it was easier to use (n=3), had a better “flow” (n=2), would be more efficient in data transfer (n=2), and meant that pen and paper were not needed (n=1). Two participants preferred pen and paper, as it was familiar (n=1) and allowed the full questionnaire to be viewed at once (n=1). One participant did not have a preference.This study used a standard qualitative methodology to assess comparability for the EORTC QLQ-H&N35, whereby comparability was judged to have been met if the participant demonstrated that they had interpreted the item in the same way for both completions. There were only a very small number of instances (3.1%) where participants interpreted the item differently on paper and electronic tablet and used a different thought process to choose a response. Overall, these findings suggest that mode of administration does not affect the way that participants respond to and interpret items in the EORTC QLQ-H&N35. While not the main focus of the study, observations and agreement between scores suggest that paper and electronic versions of the EORTC QLQ-C30 were also comparable. These findings are in alignment with existing literature regarding the EORTC QLQ-C30 and other associated modules ,18,22,23It is worth noting that there were some limitations to the study design. While there were a small number of instances where participants provided a different response across modes of administration, we acknowledge that this could easily be attributed to participant understanding of item wording or fallibility of human memory rather than the impact of the mode of administration. Furthermore, the interview procedure allowed participants a second opportunity to consider their response and question their original choice. Some participants were aware that they were changing their response between modes of administration. There were some participants who saw the double completion as a memory test and aimed to try and remember their original response on the second mode of administration, rather than treating it as a new item. Finally, it was difficult to recruit participants who were not familiar with using electronic smart devices. This is reflective of the widespread use of smart devices, across all age groups, in the United States in 2016 .Given the large amount of evidence for comparability of electronic and paper versions of the EORTC instruments and the lack of concerns identified, further comparability studies for EORTC modules that have undergone minor modifications to electronic administration are unlikely to lead to different conclusions and are probably not warranted. It is acknowledged that this study explored equivalence of paper versus PROs administered in an electronic (tablet) format, yet there exist other electronic formats  that are commonly implemented. However, meta-analyses and systematic reviews by Gwaltney et al and Muehlhausen et al conclude that the majority of comparability and equivalency studies demonstrate that the paper PRO questionnaires evaluated are quantitatively comparable with measures administered on a variety of electronic devices, including tablets and mobile phones, when minor modifications have been made ,17. ThesThis study provides evidence for comparability of the EORTC QLQ-H&N35 administered via an electronic device compared to administration via pen and paper. These findings add to the existing evidence supporting the use of electronic versions of the EORTC QLQ-C30 and associated EORTC modules to collect data in clinical trials when migrated according to best practice guidelines.


The purpose of triggering in ovulation induction is to induce the final maturation of oocytes and their release from the ovary for fertilization. The aim of the present study was to compare the effectiveness of gonadotropin-releasing hormone (GnRH) agonist and human chorionic gonadotropin (HCG) on the final maturation of oocytes and pregnancy rates in intrauterine insemination (IUI) cycles.2, LH, and FSH levels were measured at 12 and 36 hr after injection.In this randomized clinical trial, 110 infertile women who were selected for IUI entered the study. Ovulation induction was performed. Group I received 0.1 mg GnRH agonist as triggering and group II received 10,000 IU of HCG. The serum ELH surge was detected in all patients. LH levels at 12 and 36 hr after triggering was higher in Group I and it washed out earlier than group II (p=0.00). The pregnancy rate was higher in Group I, but the difference was not statistically significant . Also, the incidence of ovarian hyperstimulation syndrome was not different between the two groups (p=0.11). There was a significant difference regarding the estradiol levels at 36 hours after triggering (p=0.00). Effects of GnRH on endogenous LH surge is sufficient for oocyte releasing and final follicular maturation. Pregnancy rates and ovarian hyperstimulation syndrome incidence were not different between the groups. We suggest that GnRH agonists might be used as an alternative option instead of HCG in IUI cycles.  Ovulation induction is one of the treatments to reach a higher number of oocytes and to increase the rate of pregnancy. Induction of ovulation during intrauterine insemination (IUI) is associated with higher pregnancy rates especially in unexplained infertility, women with polycystic ovarian disease (PCOD) or in presence of a subfertile man. Final oocyte maturation and ovulation triggering are the most important steps of infertility treatment which depends on mid-cycle LH surge. Although mid-cycle FSH surge also occurs in natural cycles, its effects have been remained unclear. It seems that it has positive effects on the oocyte meiosis resumption. FSH surge promotes the LH receptor in granulosa cells as well as nucleus maturation and also increases the cumulus cells that might be helpful in the luteal phase .Standard protocol for the oocyte maturation and ovulation triggering in stimulated cycles is a bolus dose of HCG, applied when the follicles reach the appropriate size (17-18 mm). A bolus dose of HCG induces ovulation in 34-36 hr, but its significantly longer half-life of more than 24 hr vs. 60 min for endogenous LH surge, is associated with prolonged stimulation of follicles and corpus luteal as well as increases in estradiol secretion and could promote OHSS  who were candidates for IUI entered the study in Imam Hossein Medical Center, Tehran, Iran. Of this, six women were excluded from the study due to a severe abnormality of sperm or inability to give the semen on IUI day. Finally, 53 women were assigned to the intervention and 51 women were included in the control group . Inclusion criteriaPatients were candidates for IUI who were clomiphene citrate (CC) resistant (patients who do not ovulate while receiving the 150-mg dose of CC), or had less than three follicles on HCG day, or suffered from male sub-fertility or unexplained infertility . Other iExclusion criteriaAny medical diseases, severe male infertility, poor response ovaries, high response ovary (patients had three or more dominant follicles on HCG day) and any peri-tubal adhesion in hysterosalpingography or laparoscopy. None of the patients had any treatment during the menstrual cycle previous to the stimulated cycle.Hyper stimulation protocolrd day of the cycle. Stimulation consisted of 150 mg clomiphene from day 3-7 of cycle and then a daily subcutaneous injection of 150 IU of recombinant FSH  from day 8-10 of the cycle. This dose could subsequently be increased or decreased depending on the ovarian response. The follicular monitoring was performed on the 10th day and gonadotropin dose was increased or decreased according to the ovarian response until 1-2 follicles with a diameter more than 18 mm were observed.After an ultrasound scan and estradiol level determination, ovarian stimulation was routinely started on the 3The patients were then randomized to receive two different final triggering protocols using a computer based table of random numbers. Four patients in GnRH agonist group (Group I) and two patients in HCG group (Group II) were excluded due to severe abnormality of sperm or inability to give the semen on IUI day. Ovulation triggering was performed by Decapeptyle  0.1 mg subcutaneously in GnRH group and 10000 unit of HCG  in HCG group . Serum lSemen preparation and inseminationFresh sperm from the partners was used for IUI. All the samples were capacitated using swim-up protocol. IUI was systematically performed 36 hr after the HCG or Decapeptyle injection. Luteal phase supportLuteal phase support was performed by vaginal progesterone 400 mg  for 14 days. The chemical and clinical pregnancy rates, and OHSS rates were recorded and compared in two groups. Ethical considerationThe current study was approved by the institutional ethics committee review board of Shahid Beheshti University of Medical Sciences, Tehran, Iran. All participants gave informed consent before the initiation of the study. Statistical analysisAll data were analyzed by SPSS software version 22 . Descriptive data were analyzed by SD and frequency. Linear and logistic regressions were used for correction of the bias. P-value was considered significant if less than 0.05. T-test was used for paired and non-paired quantitative data. Kolmogorov-Smirnov and Wilcoxon tests, as well as contingency tables, were also used depending on the variables and their distribution. The chi-square test was used for independent groups and qualitative data. The mean age (±SD) of the women within the GnRH-a group and the HCG group was 29.4±4.3 and 29±3.9 yr, respectively (p=0.56). Body mass index in two groups were 23.3±3.2 and 23.9±3.9; indicating no statistically significant difference. There was also no significant differences in FSH levels and dose of clomiphene between the 2 groups but the total dose of gonadotropin (rFSH) was higher in group I (p=0.01). The mean number of follicles in right ovary was 5±3.7 in group I vs. 4.5±3 in group II (p=0.45). This number was 4.4±3.3 vs. 4.7±2 in left ovary on the day of HCG injection (p=0.58) . LH surgHormonal values of FSH, LH and estradiol are shown in th wk of pregnancy (Although pregnancy rate was higher in GnRH group (26.9%) in comparison with HCG group (20.8%), the difference was not statistically significant (p=0.46) . There wregnancy . Althoug2 concentrations and the number of retrieved oocytes were higher in the GnRH agonist group, the differences were not statistically significant. OHSS was not observed following the GnRH agonist usage, so even in the same patient population administered the same number of gonadotropin doses; GnRH agonist resulted in lower rates of OHSS without compromising embryo number and qualities. Based on the results, we found no differences in pregnancy, implantation and fertilization rates between the two triggering agents. Despite our finding that final EAlthough HCG is widely used for final oocyte maturation, its prolonged luteotrophic effect leads to the development of OHSS. In recent years other drugs such as GnRH agonists have been used for triggering of final maturation and ovulation especially in patients with high risk of OHSS. GnRH agonist has been used for final maturation and ovulation triggering for more than two decades especially as an alternative method for prevention of OHSS. Although it has been recognized that both GnRH agonists and exogenous HCG can induce LH surge, we could show that there are many differences in LH characteristics induced by GnRH agonist versus HCG. We observed that GnRH agonist increases the serum LH level more than HCG in 12 hr post-trigger, but drop earlier than HCG after 36 hr due to a shorter half-life. It seems that these characteristics should be helpful in the prevention of ovarian hyperstimulation syndrome. On the other side, urinary HCG promotes ovarian response and VEGF expression due to a longer half-life and increases the rate of OHSS. The flare-up effect caused by a bolus injection of GnRH agonist leads to FSH and LH release and this amount is enough for triggering the LH surge and the final maturation of the oocytes. Preliminary data has indicated that 50 mg of GnRH agonist is the minimal effective dose to trigger ovulation but the best dose is 200-500 mg . In our One of the highlights of this study was that the level of FSH before ovulation was higher in Decapeptyl group despite stopping treatment with gonadotropin, which can lead to hormonal reduction. However, the hormone’s physiological increase induced by GnRH agonists and endogenous hormone secretion might have a positive effect on growing and distribution of the cumulus cell masses and thus increase the probability of sperm entering the oocytes . NeverthIt is suggested that GnRH-a can prepare better conditions for pregnancy by causing endometrial receptivity without any effect on oocyte/embryo quality as a consequence of a more physiologic endogenous surge of gonadotropins and steroid balance. It seems that GnRH-a can be used in IUI cycles as routine even in patients that are not at risk of OHSS to improve the pregnancy rate. FSH and LH surge occur in the mid-cycle of a physiologic process and are essential for assumption of oocyte meiosis . During Unlike HCG triggering of final oocyte maturation, GnRHa triggering is a more physiological approach, eliciting a surge of gonadotropins, similar to that of the natural mid-cycle surge. Thus, in contrast to HCG triggering, GnRHa triggering induces an endogenous surge of FSH as well as LH. Natural dual endogenous LH and FSH surge with GnRH agonists are associated with a shorter half-life and there is no progress to OHSS. On the other hand, luteolysis of corpus luteum is common after GnRH agonist injection due to suppressive effect in the internal LH, especially in assisted reproductive technology. Luteal phase support with progesterone and estradiol overcome this side effect . After the GnRH triggering, a modified luteal phase support including LH-like activity (HCG) or rLH is crucial and the challenge has been to titrate the amount of LH activity needed to sustain the function of only a few corpora luteum after GnRH triggering .et al in a small study used no luteal support following a GnRH-a trigger and obtained three clinical pregnancies. However, in their study CC was used for ovarian stimulation during the follicular phase. Due to the long half-life of CC, a higher pituitary secretion of LH during the luteal phase could be expected to counteract the luteolytic action following the GnRH-a trigger. However, CC is rarely used in controlled ovarian stimulation nowadays (Gonen nowadays .HCG half-life is about 7 days, and it causes changes in the luteal phase and hence increases the VEGF levels in serum and follicular fluid which may be associated with the exacerbation of OHSS -20. SmalDespite some evidence indicating luteal phase defects and miscarriages, we surprisingly had no such findings in the current study; which can be related to the administration of vaginal progesterone. Effects of gonadotropin-releasing hormone agonist on endogenous LH surge are sufficient for oocyte releasing and final follicular maturation. Pregnancy rates and ovarian hyperstimulation syndrome incidence were not different between patients receiving gonadotropin-releasing hormone agonist or human chorionic gonadotropin. We recommend that gonadotropin-releasing hormone agonists could be used as an alternative option instead of human chorionic gonadotropin in IUI cycles.


Wearable sensors enable long-term monitoring of health and wellbeing indicators. An objective evaluation of sensors’ accuracy is important, especially for their use in health care.2Max) during running and to evaluate the accuracy of the estimated parameters  against golden reference methods.The aim of this study was to use a wrist-worn optical heart rate (OHR) device to estimate heart rate (HR), energy expenditure (EE), and maximal oxygen intake capacity  participated in a submaximal self-paced outdoor running test and maximal voluntary exercise test in a sports laboratory. OHR was monitored with a PulseOn wrist-worn photoplethysmographic device and the running speed with a phone GPS sensor. A physiological model based on HR, running speed, and personal characteristics  was used to estimate EE during the maximal voluntary exercise test and VO2Max estimated during the submaximal outdoor running test was closely similar to the sports laboratory estimate (MAPE 5.2%). The energy expenditure estimate (n=23) was quite accurate when HR was above the aerobic threshold (MAPE 6.7%), but MAPE increased to 16.5% during a lighter intensity of exercise.OHR was able to measure HR during running with a 1.9% mean absolute percentage error (MAPE). VO2Max, even during submaximal self-paced outdoor recreational running.The results suggest that wrist-worn OHR may accurately estimate HR during running up to maximal HR. When combined with physiological modeling, wrist-worn OHR may be used for an estimation of EE, especially during higher intensity running, and VO Advances in wearable sensors enable long-term monitoring of health and wellbeing indicators in various conditions and activities in both consumers and patients. Recently, significant progress in the size, power consumption, and accuracy of various different sensing technologies has led to an introduction of affordable wearable sensors with a reasonable battery life and capability to monitor, for example, physical activity, sleep, heart function, and so on. However, the reliability and accuracy of the produced information has been questioned and significant differences between different brands have been found . TherefoHeart rate (HR) monitoring provides valuable information on physiology and health status during sports, daily life, and sleep. Chest strap HR monitors have been used during sports to quantify and control training loads since the late 1980s. The main limitation for the wide and long-term use of chest strap HR monitors, especially in female users, is the discomfort that is caused by the tightness of the chest strap and possible skin irritations. Therefore, their application has remained relatively limited, especially in real-life wearable monitoring.Wearable optical HR (OHR) monitoring technology based on photoplethysmography (PPG) has been significantly improved recently because of miniaturized low-power hardware and improved embedded algorithms. OHR technology can be applied on almost any part of the body, such as on the wrist, and can hence overcome some challenges of chest strap HR monitors in their usability and long-term use. However, relatively few scientific studies have reported OHR technology performance and accuracy in laboratory or real-life conditions. Olenick et al evaluated a Mio Alpha wrist OHR device during a graded treadmill exercise test until volitional fatigue and found a strong correlation between OHR and ECG-based HR . In a stExercise HR is itself a valuable parameter. For example, it allows a real time control of training loads. However, exercise HR alone is challenging to interpret for users, and an estimation of more advanced physiological parameters during exercise would be beneficial to allow a more insightful analysis of the training. An estimation of momentary oxygen consumption and total energy expenditure (EE) for each training session and an estimation of changes in physical performance achieved by regular training are examples of these insightful parameters. An indirect calorimeter is one of the most accurate reference methods for estimating EE. This method is based on the analysis of respiratory gases and is commonly used in laboratory settings. HR has also been used for estimating oxygen consumption. Montgomery et al  evaluate2Max) of a person. VO2Max can be measured directly with an expiratory gas analyzer during a maximal voluntary exercise test. Running speed may also be used to estimate VO2Max [2Max without maximal exercise testing [2Max: −0.7 (SD 7.4%) and −3.2 (SD 7.3%) [2Max has not been widely studied.Physical performance may be estimated by the maximal oxygen consumption (VOe VO2Max . By esti testing . LeBoeufSD 7.3%) . However2Max during running and to evaluate the accuracy of the estimated parameters  against a chest strap HR and respiratory gas analysis derived from golden reference values.The objectives of the current study were to use OHR to estimate HR, EE, and VOTwenty-four healthy adults  participated in the study . The inc2Max, based on wrist PPG and mobile phone GPS data. The maximal voluntary exercise was performed to provide a standardized reference (“ground truth”) for VO2Max for each individual and to compare EE from a wrist PPG against a standard respiratory gas analysis-based EE reference during running. The order of the tests was randomized with a maximal time difference of 7 days.The study protocol included two parts: (1) a submaximal outdoor running test and (2) a maximal voluntary exercise test in the sports laboratory. The submaximal outdoor running test was performed in regular outdoor conditions in Finland with the aim of providing data from uncontrolled and sometimes challenging conditions, where subjects would train and perform their fitness tests when provided with self-testing equipment, such as a PPG wrist device and a mobile phone. The data from the submaximal outdoor running tests was used to estimate VOThe submaximal outdoor running test was performed on a pre-defined outdoor track with a flat surface. The subjects were instructed to run at a self-determined pace for at least 20 min, targeting moderate to vigorous subjectively assessed intensity, and to run 5 km. HR was monitored with an optical wrist worn heart rate monitor  and GPS data with a mobile phone . A Polar V800 HR monitor  with a built-in GPS sensor was used as a reference for the distance. The GPS reference for the distance was necessary, as the subjects performed the actual running test without continuous supervision and, hence, had a possibility to vary their running route to some extent. The PulseOn mobile app was used to track and store HR and running speed during the test. Field tests were performed outdoors between November 2014 and January 2015 in Finland in regular winter training conditions, that is, during days when it was not raining or snowing, the testing track was not too slippery to cause health risks, and the temperature was above -10 °C. The subjects were instructed to wear their own outdoor sports clothing as appropriate for the current weather during the test. These conditions are typical outdoor training conditions in Finland and, hence, provide a good benchmark for challenging real outdoor training conditions that are faced by ordinary citizens while training.2 was stabilized or started to decrease, (3) blood lactate level increased above a threshold, or (4) respiratory exchange ratio was >1.1. After the test, the subject was allowed to recover for 3 min, which was followed by a 7 min cool down jog at a self-selected speed. After this, the final blood sample was taken.The maximal voluntary exercise test was performed in a sports testing laboratory with a treadmill . The indoor temperature during the tests was 20 °C. During the test, the subjects wore a face mask from the respiratory gas analyzer , the PulseOn wrist HR device, and a chest strap HR device . The treadmill inclination was set to 0.6°. After setting up the measurement devices and instructing the user about the study protocol and the use of the treadmill, the subject performed a warm-up run at 8 km/h for 6 min. Then, the subject stood still for 6 min and the first blood sample was taken, after which the actual test started. The running speed was increased by 1 km/h, which was maintained for 3 min to reach a stable metabolism at each load. The initial running speed was set so that the predicted number of loads that the subject would be able to complete would be between 8 and 10. Between transitions, the treadmill was stopped for 20-30 s, during which a blood sample was drawn from the subject’s finger to estimate the blood lactate . The test was continued until the subject wanted to stop  or the following end criteria, based on recommendations by the Finnish Sports Testing Society, were met: (1) predicted maximum heart rate was reached, (2) measured VO2Max was calculated from the submaximal test and EE was calculated from the maximal exercise test. HR, GPS data, and personal subject information  were used for calculations. Both maximal HR estimated during the maximal exercise test and maximal HR estimated from the subject’s age  were us2) from HR. Differences in the HR-VO2 relationship during the different exercise phases (on and off phases) were included in the model. Personal maximal HR and estimated VO2Max were used for the calculation of the momentary VO2 value. EE was then estimated from VO2, respiratory quotient (RQ), and caloric equivalent [2 [2 and caloric equivalent are known, it is possible to calculate the momentary EE. The total EE can be calculated by summing up the momentary EE values.The estimation of total EE was based on a method developed earlier . Neural uivalent . RQ descalent [2 . Both ex2Max was estimated from OHR and GPS speed recorded during the self-paced running test by a company  [2 and the running speed. First, speed and OHR data are segmented to different HR ranges and the reliability of different data segments is estimated by calculating the correlation between HR and speed and comparing that to the variance of the data in that segment. In case of a wide variance and low correlation, the segment is discarded as being unreliable. Then, the most reliable data segments are used to estimate VO2Max by utilizing the relationship between HR and speed. Finally, VO2Max is estimated as the reliability weighted average of the segments.VOFinland) . The met2Max, as well as measure EE during the test. EE was measured by averaging the measured EE, based on a respiratory gas analysis for each minute. Equations defined by Weir [2Max was determined by using criteria defined by the Finnish Society of Sport Sciences [A maximal voluntary exercise test was used to determine the reference (“ground truth”) VO by Weir  were useSciences .HR data from a chest belt acquired during the laboratory test was analyzed with Firstbeat Sports software . After applying an artifact correction algorithm to the signals, the maximum HR value was observed. A second-by-second chest strap HR was used as a reference for the OHR signal during the maximal voluntary test, and the acquired maximum HR value was used as the measured maximum HR in the further analysis.t=0. Then, the HR data was averaged over 5 s non-overlapping windows. HR accuracy was estimated by the following parameters [The HR estimation accuracy of the wrist PPG device was estimated during the maximum exercise test by comparing HR from the wrist PPG device with chest strap-based HR. First, the data were re-sampled at 1.5 s sampling intervals. HR signals were synchronized in time by maximizing the cross-correlation between the signals at rameters ,4.Reliability: The percentage of time that the absolute error is smaller than 10 bpm.Accuracy: The complement of the relative error .2Max estimated with a wrist PPG device and GPS data during a submaximal test and with a gas analyzer during a maximal exercise test was compared by calculating the bias, mean absolute error (MAE), mean absolute percentage error (MAPE), and correlation coefficient  between the estimates. Bland-Altman plots were constructed to allow a visual presentation of the agreement between the two estimation methods and their average error (bias), as well as 95% confidence limits of agreement.The difference between VOThe difference between EE estimated from the wrist PPG device and respiratory gas analysis was calculated during the maximum exercise test. The analysis was carried out separately for light intensity (below aerobic threshold) and medium heavy intensity (between aerobic and anaerobic thresholds). The estimation was only performed from light to medium heavy intensity levels, as higher intensity levels can change the body acid-base balance, which can distort the indirect calorimetry method . The aert test in case normal distribution was confirmed and with the Wilcoxon signed rank test when normal distribution could not be confirmed. Pearson correlation coefficient was computed between normally distributed parameters, while Spearman rank correlation coefficient was used for the other parameters not meeting the normal distribution assumption. The strength of the correlation coefficients was interpreted based on the following definitions: weak (r ≤.5), moderate (r=.5–.7) and strong (r ≥.7). All statistical tests were performed as two-sided and the level of significance was set at P<.05.The normal distribution of data was examined by the Shapiro-Wilk test. The difference between the methods was tested with a paired All data analysis was carried out with MathWorks Matlab (version 8.5). All statistical testing was carried out with IBM SPSS statistics (version 22).HR estimated with a wrist PPG device appeared to closely follow HR monitored with a chest strap . In most2Max estimated with a wrist PPG device and phone GPS data with a PulseOn app was close to VO2Max measured during maximum exercise tests in laboratory conditions , when measured maximum HR was used in the estimation. The distance estimated by a phone GPS was underestimated on average by 5.0% (−270m)  . Howeverncreased . There wData from one male subject was excluded from the EE estimation analysis due do failure in respiratory gas analysis data acquisition, and results are reported for the remaining 23 subjects. Error in the EE estimation was lower MAPE 6.7%) in the higher intensity exercise , but increased in lower intensities .7% in th. A wrist2Max based on wrist PPG and phone GPS speed and evaluated their accuracy during running based on golden reference methods. OHR appeared to be accurate during running; the MAPE was 1.9% and reliability 95.4% during a maximal voluntary exercise test. This is well in line with the earlier results [We estimated HR, EE, and VO2Max developed earlier by a company , which is widely available in various sports products. EE estimation with this method has been validated earlier [r=.97). The results are comparable to a similar study by Robertson et al [r=.57) to strong (r=.85) correlations during low and high intensity exercise, respectively. However, significant differences between different OHR devices have been reported. Recently, Wallen et al studied the EE estimation accuracy of four different OHR devices against indirect calorimetry and found only one device (Samsung Gear S) to have a strong correlation (r=.86) with the reference, while the other three devices exhibited only a weak correlation to reference EE [The accuracy of more advanced parameters estimated from OHR is dependent, both on the accuracy of the OHR and on the validity of the analytical models. We used an HR-based estimation of the EE, and an HR and running speed-based estimation of the VO earlier , suggest earlier . For com earlier . The EE on et al , who useon et al  and reporence EE . Our res2Max. We used OHR and a mobile-based speed estimation to estimate VO2Max during self-paced outdoor running in real and challenging outdoor conditions during winter in Finland. These conditions may be considered the “worst case” training conditions and, for example, the temperature difference may increase the observed estimation error for VO2Max. The analytical method was based on the well-known HR versus speed relationship and on detecting the most reliable data periods for VO2Max estimation during the exercise [2Max estimation, that is, respiratory gas analysis acquired during a maximal voluntary exercise test in a sports laboratory. The results suggest that OHR and speed-based VO2Max estimation during self-paced running are able to quite accurately estimate VO2Max, even in these challenging outdoor conditions; we found a MAPE of 5.2%  for VO2Max when an individually measured HR maximum was used in the estimation. When age-estimated maximum HR was used, the error increased slightly. A significant contribution to the inaccuracy originated from phone GPS tracking, which underestimated the distance by 5% on average and led to a corresponding underestimation of the VO2Max. In addition, during the outdoor testing, there were challenging weather conditions (cold and winter), which posed challenges for PPG HR estimation because of potentially poor perfusion, increasing the potential error for the OHR during field conditions. These weather conditions may also have affected the real VO2Max. Also, differences in running efficiency affect the correspondence between the running speed and the true physical load, and, hence, increase the error in HR and speed-based VO2Max estimation. There was also a tendency for the OHR and speed-based analysis to overestimate the VO2Max in individuals with a lower real VO2Max. In summary, the results suggest that the method may be used to estimate VO2Max relatively accurately during self-paced running, even in challenging outdoor conditions.The level of fitness may be quantified by the estimation of VOexercise . We comp2Max estimation accuracy, based on OHR data. We used a realistic or even challenging setting (self-paced outdoor running in winter) to estimate VO2Max. This is a setting that can be applied by an ordinary user, and as such, the method can be directly applied by healthy users to estimate their fitness levels. We used the golden standard  as a reference for EE and VO2Max. The main weakness of the study is that it had a relatively small study population; however, despite this, the results can be considered to be at least indicative. In addition, the outdoor tests were carried out in a challenging environment , increasing the error of the outdoor VO2Max estimation. On the other hand, this provides the worst case scenario, and the results were still within an acceptable error margin. Finally, the study included only one wrist OHR device, which limits the generalizability of the results. Only a single device was used for practical reasons—wearing several devices in both laboratory and outdoor conditions would have complicated the study implementation. The PulseOn device was chosen for the study because, at the time of data collection, to our knowledge, other available wrist OHR devices did not support estimation of VO2Max together with accurate data logging capability. However, the results are not without generalizability. The applied VO2Max and EE estimation algorithm [This study has several strengths, but also weaknesses. To our knowledge, this is the first study to report both EE and VOlgorithm ,20 has blgorithm , is comm2Max during running and evaluated their accuracy against golden standard methods. The results show that current high-end wrist OHR devices may provide accurate HR that can be compared with a chest strap HR, during running, up to a maximum HR. When combined with proper analytics, OHR may be used to quite accurately estimate EE, especially during moderate to medium heavy intensity activities. An estimation of VO2Max during self-paced outdoor running using OHR and a mobile phone’s GPS data and proper HR analytics also allows a relatively accurate estimation of a fitness level (VO2Max). Wrist PPG devices accompanied by phone apps provide a reliable alternative for training monitoring in realistic conditions.We applied a commercially available OHR device to estimate HR, EE, and VO


The Modified Physiological Triage Tool (MPTT) is a recently developed primary triage tool and in comparison with existing tools demonstrates the greatest sensitivity at predicting need for life-saving intervention (LSI) within both military and civilian populations. To improve its applicability, we proposed to increase the upper respiratory rate (RR) threshold to 24 breaths per minute (bpm) to produce the MPTT-24. Our aim was to conduct a feasibility analysis of the proposed MPTT-24, comparing its performance with the existing UK Military Sieve.A retrospective review of the Joint Theatre Trauma Registry (JTTR) and Trauma Audit Research Network (TARN) databases was performed for all adult (>18 years) patients presenting between 2006–2013 (JTTR) and 2014 (TARN). Patients were defined as priority one (P1) if they received one or more LSIs. Using first recorded hospital RR in isolation, sensitivity and specificity of the ≥24 bpm threshold was compared with the existing threshold (≥22 bpm) at predicting P1 status. Patients were then categorised as P1 or not-P1 by the MPTT, MPTT-24 and the UK Military Sieve.The MPTT and MPTT-24 outperformed existing UK methods of triage with a statistically significant (p<0.001) increase in sensitivity of between 25.5% and 29.5%. In both populations, the MPTT-24 demonstrated an absolute reduction in sensitivity with an increase in specificity when compared with the MPTT. A statistically significant difference was observed between the MPTT and MPTT-24 in the way they categorised TARN and JTTR cases as P1 (p<0.001).When compared with the existing MPTT, the MPTT-24 allows for a more rapid triage assessment. Both continue to outperform existing methods of primary major incident triage and within the military setting, the slight increase in undertriage is offset by a reduction in overtriage. We recommend that the MPTT-24 be considered as a replacement to the existing UK Military Sieve. The Modified Physiological Triage Tool (MPTT) was derived on a military cohort using logistic regression and outperforms all existing triage tools at predicting the need for life-saving intervention in both military and civilian populations.Increasing the upper respiratory rate threshold to 24 (MPTT-24) allows for a reduction in the time required to use the triage tool.Using the Alert; responds to Verbal stimulus; responds to Painful stimulus; Unresponsive (AVPU) scale as supposed to the GCS to measure conscious level will enable the MPTT-24 to be used by a greater number of personnel, increasing its applicability.Performance of the MPTT-24 is largely unchanged from the MPTT, and it clinically and statistically outperforms the existing UK Military Sieve at predicting the need for life-saving intervention.We recommend that the MPTT-24 be considered as an alternative to the existing UK Military Sieve for the purposes of primary major incident triage in the military setting.Triage is the process of prioritising patients on the basis of their clinical acuity and is a key principle of effective major incident management.The UK Military Sieve and National Ambulance Resilience Unit Sieve are the algorithms used by the Defence Medical Services and Ambulance Services, respectively, for primary major incident triage.A number of studies have shown that existing methods of triage have limited accuracy at predicting the need for life-saving intervention in both the military and civilian environments.Respiratory Rate (RR) and Glasgow Coma Scale (GCS) form key components of the MPTT and can both be time consuming to accurately measure, with significant inter-rater reliability being described previously.Accepting a more pragmatic approach—with a threshold RR which is easily calculated within a shorter time frame—may change the test characteristics of the MPTT. The aim of this study was to conduct a feasibility analysis of the proposed MPTT-24 and compare its test characteristics with both the original MPTT and the existing UK Military Sieve.>18 years) patients presenting between 2006–2013 (JTTR) and 2014 (TARN).A retrospective review of the Joint Theatre Trauma Registry (JTTR) and Trauma Audit Research Network (TARN) databases was performed for all adult  if they had received one or more life-saving interventions from a previously defined list, derived through international consensus of experts involved in major incident management .14 UsingThe use of the JTTR was approved by the Medical Directorate, Royal Centre for Defence Medicine. Additionally, as part of a larger programme of work, this study received ethical approval from the Human Research Ethics Committee of the University of Cape Town, the primary academic institution of the lead author (reference 285/2013).Basic study characteristics are shown in When incorporated into the MPTT-24, an absolute reduction in sensitivity was observed  with an increase in specificity. There was a statistically significant difference between the MPTT and MPTT-24 in the way they categorised TARN and JTTR cases as P1 (p<0.001).The MPTT-24 demonstrated a statistically significant (p<0.001) increase in sensitivity  over the existing UK Military Sieve in its ability to identify those in need of a life-saving intervention.In this study, we have demonstrated that pragmatic modifications to the MPTT, in the form of the MPTT-24, can be implemented while maintaining comparable performance at predicting the need for life-saving intervention in both civilian and military trauma registry populations. With these modifications, the MPTT-24 continues to outperform the existing UK Military Sieve.In keeping with the existing UK Military Sieve,The MPTT assesses the patient’s conscious level using the GCS, with patients scoring 13 or lower being considered P1. While this represents the optimum threshold of conscious level at predicting need for life-saving intervention, it is not without limitations.A key principle of primary major incident triage is that it can be conducted rapidly and measuring the respiratory rate can be time consuming. By increasing the upper respiratory rate threshold of the MPTT, users are able to measure the respiratory rate over a 15 second period, allowing for a potential reduction in the time required to prioritise patients with the MPTT-24 by up to 15 seconds. If this reduction is applied to a theoretical scenario with 20 patients requiring triage, then up to 5 min could be saved by using the MPTT-24 rather than the MPTT. However, we acknowledge that this increased threshold is unlikely to convey any additional time benefit if users choose to measure the respiratory rate over a 30 second period.Adopting the MPTT-24 comes at the expense of a reduction in sensitivity and therefore a higher rate of undertriage (1 - sensitivity). Clinically, this increased rate in undertriage is negligible; within the military setting, 30 genuine P1 patients would need to be assessed before an additional patient is undertriaged by the MPTT-24. Likewise, the reduction in overtriage is negligible between the MPTT and MPTT-24 and needs even greater number of patients before a difference is observed.In the civilian setting, the rate of overtriage for both the MPTT-24 and MPTT is high . Although this is comparable to the overall overtriage rate following the London 7/7 attacks (64%),A key limitation of our study is the use of first recorded hospital physiology to calculate triage priorities. While prehospital data is recorded on both the JTTR and TARN databases, complete data are available for only 16.7% and 37.2% of military and civilian cases, respectively, making prioritisation with prehospital data unreliable. However, when complete hospital and prehospital physiology were compared in both datasets, we observed that the median and IQR were almost identical.When compared with the existing MPTT, the MPTT-24 allows for the potential for a more rapid triage assessment, while maintaining comparable performance and continuing to outperform existing methods used in the UK. Within a military setting, the slight increase in undertriage is offset by a reduction in overtriage. We recommend that the MPTT-24 be considered as a replacement to the existing UK Military Sieve for the purposes of primary major incident triage.