In addition, children’s ability to comprehend visual-only speech (speechreading ability) is related to phonological awareness and reading skills in both deaf and hearing children. We tested whether training speechreading would improve speechreading, phoneme blending, and reading ability in hearing children. Ninety-two hearing 4- to 5-year-old children were randomised into two groups: business-as-usual controls, and an intervention group, who completed three weeks of computerised speechreading training. The intervention group showed greater improvements in speechreading than the control group at post-test both immediately after training and 3 months later. This was the case for both trained and untrained words. There were no group effects on the phonological awareness or single-word reading tasks, although those with the lowest phoneme blending scores did show greater improvements in blending as a result of training. The improvement in speechreading in hearing children following brief training is encouraging. The results are also important in suggesting a hypothesis for future investigation: that a focus on visual speech information may contribute to phonological skills, not only in deaf children but also in hearing children who are at risk of reading difficulties. A video abstract of this article can be viewed at  ChildrePhonological awareness tasks require children to manipulate the sublexical structure of words. Such tasks are believed to relate to reading because they assess the quality and specificity of abstract representations of the phonological structure of spoken words. Typically, these underlying phonological representations are thought of as depending on interactions between auditory speech input and articulatory outputs. However, information about speech is also conveyed via vision; particularly information about the place of articulation of sounds. Visual speech that is congruent with auditory speech enhances speech perception in adults  and chilIn hearing infants, speech perception abilities have been shown to relate to broader language skills. Visual speech perception influences infants’ word-form recognition  and someGiven the link between visual speech perception and phonological awareness, it is perhaps also not surprising that speechreading ability relates to reading ability in hearing children  as well To investigate whether visual speech information contributes to the development of phonological awareness skills in hearing children, it is important to first establish whether speechreading skills can be improved in hearing children. Some researchers have claimed that speechreading is a “hard-wired” skill that cannot be trained e.g., . HoweverThe primary goal of the current study was to assess whether speechreading can be trained in hearing children. A secondary question was whether such speechreading training, including visual-only phoneme blending games, would also lead to improvement on phonological awareness tasks. The STAR training programme was adapted for use with 4-5-year-old hearing children based on the deaf children’s performance in the We recruited 92 hearing children aged 4-5 years old. Half the children (training group) completed three weeks of speechreading training games and the other group acted as business-as-usual (BAU) controls. Before and after training both groups were tested on measures of speechreading, phonological awareness, and single-word reading, which were comprised of a set of words used in the training and a matched set of untrained words.For each of the single word speechreading, phonological awareness, and single-word reading tasks we predicted that the speechreading trained group would perform better than the control group on post-tests when controlling for baseline scores on that test and that this would generalise to untrained words as well as trained words. We also predicted that the speechreading trained group would perform better than the control group on a measure of sentence speechreading involving unseen models and stimuli.22.1https://osf.io/wyc84, modified analysis plan: https://osf.io/8kh9m). Ethical approval for the study was granted by the UCL Research Ethics Committee (3612/006) and informed consent was provided by parents for the participating children. This study was a randomised experiment with hearing children aged 4 to 5 years old. Children were assessed at pre-test (T1) on measures of speechreading, phonological awareness, and single-word reading. Pre-test assessments took place in the week before the intervention commenced. Children were then randomised to the speechreading intervention group or to a BAU control group. All children were then re-assessed again in the week immediately after the 3-week intervention period (T2). After the T2 assessment it was decided to follow-up the children after 3 months (T3) to assess whether the intervention effects were maintained. The T3 assessments were not included in the pre-registration (https://osf.io/wyc84). Therefore, the T3 analyses are reported here as exploratory. The intervention was run by the experimenter and therefore was not blinded.The design for this study was pre-registered on the Open Science Framework . The remaining 86 children  had a mean age of 4 years and 11 months  at baseline. The children participated in this study in the second term of their first year at school. Pilot work indicated that at the beginning of their first term of the first year at school children were not able to complete any of the assessment tasks but children in the first term of Year 1 (second year at school) were already at ceiling on many of the phonological awareness tasks. By the end of their first term at school, pilot children were able to attempt most of the tasks. Therefore, conducting the intervention in the second term of school was considered appropriate.Ninety-two 4-to-5-year-old hearing children were recruited from five mainstream schools (seven classes) in Cambridgeshire and London. The CONSORT diagram in t(84) = 0.06, p = 0.953, d = 0.01) or performance on any of the three phonological awareness tasks at baseline (Syllable blending: t(84) = 1.25, p = 0.217, d = .27; Phoneme Blending: t(84) = –0.31, p = 0.754, d = –0.07; Phoneme Deletion: t(84) = –0.36, p = 0.721, d = –0.08).The children were randomised using stratified randomisation within classes and schools in Stata . There were 43 children  in the intervention group and 43  in the BAU control group. There were no differences between the groups in age (2.3All children were assessed at baseline (T1), immediately after the intervention time (T2) and 3 months after the intervention (T3) on vocabulary, speechreading, phonological awareness, and single-word reading measures. All these measures included 20 words from the intervention (trained words) and the matched list of untrained words. Including 20 words in each list provided a focused set of training words whilst maintaining a sufficient number of training words to avoid excessive repetition. The trained and untrained items were alternated trial by trial throughout the assessment tasks.2.3.1Two lists of 20 words were compiled from a total of 103 words from 2.3.2The vocabulary task consisted of images of all 40 items from the trained and untrained sets. Children were asked to name each item individually. If a child provided a similar label  they were prompted to provide a different label (“Can you think of another word for that?”). If the child could not name an item, the correct label was provided. The vocabulary task was only used at pre-test as a screening measure to ensure the children had a suitable proficiency in English. Those who scored less than two standard deviations below the mean on the vocabulary measure at pre-test were not further included in the trial. All remaining children had a minimum score of 31/40 on the vocabulary task.2.3.3The single word speechreading task was made up of silent videos of four models speaking all 40 items. The videos were ordered in terms of ease of speechreading , with thhttps://dcalportal.org/). In this task, the children watched 12 silent videos of a speaker  asking an everyday question, such as “How old are you?”. After each video the child was asked to repeat the question and were awarded one point for each word correctly identified, regardless of word order. Children could score a total of 62 points. For full details see https://doi.org/10.23641/asha.8856356). This task was not included in the exploratory 3-month follow-up (T3) in order to reduce testing time.Sentence speechreading was assessed with the “Everyday Questions” subtest of the Test of Child Speechreading , shown on the screen four at a time. The words were ordered based on orthographic complexity and grapheme-phoneme correspondence and the trials alternated between trained and untrained items. Each child was asked to work through the screens reading the words aloud. No feedback was given. If five consecutive words were read incorrectly the task was stopped.2.4The speechreading training game was adapted from The children played the games in a room with 1–4 other players at a time, each playing on their own touch-screen device (Microsoft-Surface Pro) via the internet. These sessions were supervised by the experimenter. In every game the trials only progressed once the child had selected the correct response. If a child responded incorrectly they had to re-watch the video before being able to select any of the response options. The games progressed using an adaptive algorithm based on each child’s performance. The games became progressively more difficult in two ways: (1) by changing the level of support (text on the screen) and (2) the similarity of the distractors to the target. These features are further detailed in the 2.5https://www.gov.uk/education/phonics).The BAU control group were seen by the experimenter at pre- and post-test only. As children in the intervention group participated at different times during the school day, the BAU control group were doing a variety of school activities during the intervention period. All children (intervention and control) received 30 min of phonic reading instruction in school every day in line with the National Curriculum in the UK. The BAU control group did not receive any additional intervention. Children in Reception (aged 4/5 years) receive daily phonics lessons, which include recognition of individual letters and digraphs and blending of words. The classes aim to prepare the children to be able to decode words by the end of year 1 (aged 6), which is assessed by the phonics screening check involving reading 20 words and 20 pseu-dowords . Each ANCOVA was run three times: once with both trained and untrained words included, once with only trained words, and once with untrained words. Performance on each of the measures was considered to be normally distributed based on visual inspection of Q-Q plots. The assumption of equality of slopes was assessed by including an interaction term in the ANCOVA models between the covariate and the group.3Assessment data were collected from 86 children at T1 , 85 children at T2 , and 74 children at T3  (see d for each group at baseline (T1), immediate post-test (T2) and 3-month follow-up (T3). d was calculated as the difference in gains between groups from T1 to each post-test divided by the pooled standard deviation at T1.3.13.1.1t = 3.60, p < 0.001); trained words  and untrained words .Separate ANCOVAs of T2 single word speechreading scores, controlling for baseline single word speechreading scores, showed an advantage for the intervention group over the BAU control group for all words .An ANCOVA of T2 TOCS Extension scores, controlling for baseline TOCS extension scores, showed no significant difference between the speechreading intervention and BAU control groups  with each other. Syllable blending was excluded as a measure due to ceiling effects  and so were therefore not combined. The planned analyses (ANCOVA and t-tests) are therefore reported for each measure independently below.The pre-registration specified that performance on the three phonological awareness tasks would be combined if they showed a strong correlation ; trained words  and untrained words .Separate ANCOVAs of T2 phoneme blending scores, controlling for baseline phoneme blending scores, showed no significant difference between the speechreading intervention and BAU control groups on all words . However, for children scoring 1 standard deviation below the mean at pre-test  there was a significant advantage for the intervention group at post-test . These results must be interpreted with caution as there were few children scoring at the lower end on this measure. However, this pattern suggests that the speechreading training was effective for children with low initial phoneme blending scores.The assumption of equal slopes was not met for the phoneme blending task (trained and untrained words combined), the interaction term between the group and covariate was significant  as shown in t = 0.605, p = 0.547); trained words  and untrained words .Separate ANCOVAs of T2 phoneme deletion scores, controlling for baseline phoneme deletion scores, showed no significant difference between the speechreading intervention and BAU control groups on all words ; trained words  and untrained words .Separate ANCOVAs of T2 single-word reading scores, controlling for baseline single-word reading scores, showed no significant difference between the speechreading intervention and BAU control groups on all words  to determine whether the improvement on speechreading skill was maintained and whether there was a knock-on effect on phonological awareness and single-word reading ability. The analyses reported at T2 were repeated with T1 and T3 scores.3.2.1t = 2.40, p = 0.019); trained words  and untrained words .Separate ANCOVAS of T3 single word speechreading scores, controlling for baseline single word speechreading scores, showed an advantage for the intervention group over the BAU control group for all words  trained words  or untrained words .Separate ANCOVAS of T3 phoneme deletion scores, controlling for baseline phoneme deletion score, showed no significant difference between the speechreading intervention and BAU control groups for all words ; trained words  or untrained words .Separate ANCOVAS of T3 single-word reading scores, controlling for baseline single-word reading score, showed no significant difference between the speechreading intervention and BAU control groups  and whether this training transfers to improvements in phonological awareness.4.1As predicted, speechreading training led to improvements in speechreading performance in hearing 4–5 year old children. The speechreading intervention group performed better than the BAU control group on the single word speechreading post-test when controlling for baseline scores on the same test. In addition, the speechreading intervention group not only improved on trained words but also on untrained words as a result of the intervention. These effects were maintained at the 3-month follow-up.These results challenge the idea that speechreading is a fixed skill that cannot be trained . There iAlthough improvements were seen on the single word speechreading task, the speechreading training did not result in improved performance on the Test of Child Speechreading (TOCS) Everyday Questions extension task (repetition of everyday questions). This may be due to the differences between the tasks. Unlike the TOCS extension everyday questions task, the single word speechreading task had a similar format to the training games, involving the same talkers, multiple-choice responses, and single-word stimuli. First, it is possible that the very brief training would not extend to untrained talkers . Second,The improvements seen on single word speechreading but not on sentence-level speechreading (everyday questions test) as a result of the intervention in hearing children are in contrast to the assessment of the STAR programme with deaf children, who showed the opposite pattern STAR_D, . Both thOne reason for the difference in results between the current study and the STAR_D project may be that the training was over three times longer in the STAR_D project  than in the current study . In addition, the children in the STAR_D training played some games that used sentences, for example “Find the sheep.”. It may be that the longer period of training and the exposure to sentences in the training is necessary to lead to improvements in the more complex sentence-level speechreading as tested in the TOCS Everyday Questions extension task.4.2Having shown improvement in speechreading, which generalised to untrained words, following training our secondary aim was to investigate whether speechreading gains would lead to improvements in phonological awareness skills. At the group level there were no effects of speechreading training, with no differences in performance between the speechreading training group and the control group on post-tests of either phoneme blending or phoneme deletion when controlling for baseline scores. This was the case for both trained and untrained words. Follow-up analyses three months later also showed no improvement at the group level in phoneme deletion scores as a result of the intervention. However, despite no group effects on performance on phonological awareness tasks, the poorest performing children did show improvements on phoneme blending as a result of training.At the group level, an effect on phonological awareness may not have been observed as the improvement in single word speechreading was moderate, and therefore may not have been large enough to lead to changes in phonological awareness. Perhaps more importantly, both the speechreading intervention and the BAU control groups made large improvements from baseline to both post-test timepoints on the phoneme deletion task, reflecting the fact that phonics instruction is a dominant aspect of the curriculum for this age group . The curAlthough many children make rapid progress in phonological awareness skills in their first few months of school, many children struggle. It is likely to be specifically these children that may benefit from their attention being brought to visual speech in order to augment and support their understanding of the sublexical structure of words through sound alone . The chi4.3The speechreading training group did not differ in performance on the single-word reading post-tests from the BAU control group at the immediate follow-up or the 3-month follow-up, on either trained or untrained items. It is not surprising that there were no differences in single-word reading as a result of the intervention as single-word reading is strongly predicted by phonological awareness ability in hearing children, and, in the children as a group, this did not improve as a result of the intervention . As with4.4There are some features of the design that limit the interpretation of the results. The small training dosage meant that any improvements in phonological awareness as a result of the intervention were likely to be small. During the same period as the intervention many children made huge improvements in their phonological awareness skills in both groups and therefore small intervention effects would be difficult to observe. Future studies targeting those with poor phonological awareness skills would address this limitation. Another limitation of the design was that the experimenters conducting the post-tests also oversaw the training sessions. Therefore, they were not blinded to the conditions the children were assigned to, which could bias the results.5This study demonstrated the efficacy of the STAR intervention in young hearing children, showing that speechreading can be trained in hearing children as well as deaf children  even witMovie S1Supplementary material


Parkinsonism is common in people with dementia, and is associated with neurodegenerative and vascular changes in the brain, or with exposure to antipsychotic or other dopamine antagonist medications. The detection of parkinsonian changes to gait may provide an opportunity to intervene and address reversible causes. In this study, we investigate the use of a vision-based system as an unobtrusive means to assess severity of parkinsonism in gait.Videos of walking bouts of natural gait were collected in a specialized dementia unit using a Microsoft Kinect sensor and onboard color camera, and were processed to extract sixteen 3D and eight 2D gait features. Univariate regression to gait quality, as rated on the Unified Parkinson’s Disease Rating Scale (UPDRS) and Simpson-Angus Scale (SAS), was used to identify gait features significantly correlated to these clinical scores for inclusion in multivariate models. Multivariate ordinal logistic regression was subsequently performed and the relative contribution of each gait feature for regression to UPDRS-gait and SAS-gait scores was assessed.Four hundred one walking bouts from 14 older adults with dementia were included in the analysis. Multivariate ordinal logistic regression models incorporating selected 2D or 3D gait features attained similar accuracies: the UPDRS-gait regression models achieved accuracies of 61.4 and 62.1% for 2D and 3D features, respectively. Similarly, the SAS-gait models achieved accuracies of 47.4 and 48.5% with 2D or 3D gait features, respectively.Gait features extracted from both 2D and 3D videos are correlated to UPDRS-gait and SAS-gait scores of parkinsonism severity in gait. Vision-based systems have the potential to be used as tools for longitudinal monitoring of parkinsonism in residential settings. Older adults with dementia are more likely to develop gait disorders than those without dementia, including the development of parkinsonism . While sCurrently, parkinsonism in gait is assessed using the gait criterion of the Unified Parkinson’s Disease Rating Scale (UPDRS), and when it is medication-induced, by the gait criterion of the Simpson-Angus Scale (SAS) , 5. HowePrevious work on quantifying parkinsonism in gait has focused on patients with Parkinson’s disease (PD) and has not included individuals with dementia who exhibit parkinsonian gait. Studies on quantitative gait analysis in patients with PD have primarily relied on wearable sensors for data collection , 7. HowePrevious studies have used 3D joint locations obtained from Microsoft Kinect sensors to analyze gait of individuals with PD –13. ThesHowever, this previous work relies on the Kinect, whose depth sensor can only track individuals at a distance between 0.5 m to 4.5 m, and experiences discontinuities in predicted joint position when a joint is occluded from view of the sensor , 15.Studies using standard video for detection of parkinsonian gait have focused on creating binary masks of the participant through background subtraction , 17. Whiseverity of parkinsonian gait on clinical scales has not yet been explored using pose-tracking in video.Recent advances in pose tracking algorithms that use deep learning methods have facilitated extraction of joint positions from videos recorded using consumer-grade color cameras . BecauseThe aim of this study is to evaluate whether vision-based systems can be used to identify gait features associated with the severity of parkinsonism in people with dementia, as rated on the UPDRS-gait and SAS-gait scales. It is hypothesized that both 2D and 3D gait features capture clinically relevant aspects of gait and are thus both correlated with parkinsonism severity in gait, rated on the UPDRS-gait and SAS-gait scales.The data used for this investigation was collected as part of a larger prospective observational study conducted at a specialized dementia unit . All patThe data collection system consisted of a Microsoft Kinect for Windows v2 sensor ceiling-mounted in a hallway of the dementia unit. The Kinect was used to simultaneously record standard color video, as well as 3D skeletons using additional data from the onboard depth sensor. A Radio Frequency Identification (RFID) system was used to protect the privacy of non-participating individuals by only collecting data when study participants entered the field of view of the Kinect . The KinA set of 14 participants from the larger study were purposively selected for inclusion in this study, such that the sample included a range of parkinsonian gait characteristics. Data was collected over the course of several weeks for each participant who were undergoing changes in treatment with antipsychotic medication; so it was possible for the person’s gait to change from one recording to another. When multiple walking bouts of a participant were captured by the data collection system on a single day, one of the walking bouts for that day was randomly selected. This was done to incorporate longitudinal data collected over the course of several weeks, while also avoiding biasing the dataset with many walks collected on the same day, as walks collected in close succession are postulated to be of similar quality, and therefore have the same clinical scores.Each selected walking bout was scored on the gait criterion of the UPDRS and SAS scales by an expert annotator. Both the UPDRS and SAS use an integer scale from 0 to 4 to evaluate gait, where a higher score indicates the presence of gait characteristics consistent with more severe parkinsonism.Gait parameters were computed from the recorded gait sequences in two ways: using the 3D joint coordinates extracted using the Kinect software development kit (SDK), and by directly processing the recorded color videos via the OpenPose library to obtain 2D joint (pixel) coordinates .Gait parameters computed from the 3D joint coordinates include spatiotemporal, variability, symmetry, and mechanical stability measures of gait. A complete list of the 3D features investigated in this study is presented in Table Furthermore, gait parameters were also calculated directly from the videos recorded using the color camera of the Kinect. The first step of this process involved extracting 2D keypoints corresponding to the joint locations of the individuals in the scene. A TensorFlow implementation of the OpenPose library, pretrained on the MPII dataset was used for keypoint extraction . Joint cTo calculate these gait features, the steps in each walking bout were first identified. This was done by differentiating the vertical position of the ankle keypoints with respect to time, and as proposed in , denotinNote that for the 2D feature set, it is not possible to compute gait features that rely on distance measures in the depth (anterior-posterior) direction (such as step length or velocity) using the video stream. However, to normalize for the distance of the participant with respect to the camera over the course of a walking bout, all horizontal and vertical distances (in pixels) computed using the 2D keypoints were divided by the distance between the left and right hip keypoints in that frame.Overall, 16 gait parameters were computed from the 3D joint keypoints, and 8 gait parameters were computed from the 2D joint keypoints.p < 0.05. The goal of this univariate analysis was to select gait parameters to include in the subsequent multivariate regression model. Thus, Bonferroni correction was not applied to avoid prematurely discarding gait features that were truly correlated with the dependent variables.During preliminary analysis, univariate ordinal logistic regression was performed between each gait parameter and the dependent variable (UPDRS-gait or SAS-gait score). The threshold for statistical significance was set at r, between all gait features were computed. When the magnitude of the correlation coefficient was greater than 0.5, only the gait feature with the larger magnitude correlation to the dependent variable was retained for inclusion in the multivariate model. Therefore, the final multivariate model included the demographic features of age and sex, as well as all of the gait parameters that were significantly correlated with the dependent variable (either UPDRS-gait or SAS-gait score), but not strongly correlated with each other. This statistical analysis was repeated twice, analyzing the set of gait features computed from the 2D and 3D joint locations separately.A multivariate analysis was then performed to evaluate the relative importance of the gait features selected in the univariate analysis for regression to UPDRS-gait and SAS-gait scores. The multivariate models contained the demographic features of age and sex, as well as all gait features that were identified as being significantly correlated to the dependent variable in the univariate analysis. To avoid including gait features that are strongly correlated with each other, Pearson correlation coefficients, A total of 401 walking bouts from 14 participants  were analyzed. Table All 401 bouts had associated Kinect data, however color video was only available for 364 bouts. In 19 bouts, the participant could not be tracked by the OpenPose algorithm, and 96 walks were too short (less than 3 tracked steps), leaving 249 bouts for which 2D gait features were computed, and 398 bouts for which 3D gait features could be calculated. The walks for which 2D gait features were calculated are a subset of the walks for which 3D gait features were computed. However, the mean number of steps per walk captured by the 2D features was 6.9, whereas the 3D gait features captured 5.8 steps per walk. Table p-values for univariate regression to UPDRS-gait and SAS-gait scores using 2D and 3D gait features, respectively.Table The 2D gait features that were significantly correlated to UPDRS-gait score were the number of steps in the walking bout, cadence, and the symmetry index and coefficient of variation of step time. The 2D gait features correlated to SAS-gait were the same as those for UPDRS-gait, with the addition of the average margin of stability (MOS). The 3D gait features that were correlated to UPDRS-gait were: walking speed, symmetry of step width and length, root mean square (RMS) of medio-lateral velocity of the sacrum, average MOS, and step width. The same 3D gait features were correlated with SAS-gait scores, with the addition of step time symmetry.p-value for a particular gait feature, the higher the probability that including that gait feature significantly improved the regression model to the clinical variable. When considered individually, no gait features significantly (p < 0.05) improved regression to UPDRS-gait score. Conversely, for the regression model to SAS-gait, the cadence, coefficient of variation (CV) of step time, and average MOS all significantly improved the model (p < 0.05) when considered individually.Table A similar multivariate regression model was developed for using the 3D gait features selected during the univariate analysis. Table Figure The accuracy of the multivariate regression models by participant is presented in Table By analyzing natural walking bouts collected from 14 patients over the course of several weeks, this study has identified the gait features that are significantly associated with parkinsonism severity in gait of individuals with dementia, as rated on the UPDRS and SAS scales. Both 2D and 3D gait features capture clinically relevant data that can be used to quantify the severity of parkinsonism in natural gait, so there is an opportunity to use vision-based systems for longitudinal assessment of parkinsonian gait in residential care settings.Spatiotemporal, variability, and symmetry measures of gait extracted from 2D and 3D video data were found to be significantly associated with UPDRS-gait and SAS-gait clinical measures. These features are consistent with previous studies where marker-based vision systems were used to quantify differences in gait of healthy individuals and those with PD. In particular, decreased stride length and gait velocity, and increased bilateral asymmetry have been found to be significantly associated with parkinsonian gait –32. A liFurthermore, while previous work has demonstrated the feasibility of using gait features extracted using 3D Kinect skeletons to classify parkinsonian gait into three levels of severity , to the The UPDRS-gait regression models incorporating 2D or 3D gait features achieved similar average final accuracies . Likewise, the SAS-gait models achieved final accuracies of 47.4 and 48.5% with 2D or 3D gait features. As seen in Table As observed qualitatively in the confusion matrices in Fig. We have also demonstrated that both the Kinect sensor and onboard color camera provide similar results, suggesting that consumer-grade video cameras are sufficient to capture clinically significant characteristics of parkinsonian gait. Given the similar accuracy of the models with 2D and 3D gait features, the advantage of using 2D gait features is that they can be computed from video collected with any standard consumer-grade camera, whereas the 3D gait features require specialized imaging systems with depth sensors such as the Microsoft Kinect. For low-cost or mobile applications, or in instances where the individual is more than 4.5 m from the camera, 2D gait features are better suited to quantify severity of parkinsonism in gait. Conversely, for applications where it is possible to use larger and more sophisticated imaging systems, 3D features may be better suited as they can also capture spatiotemporal gait features that rely on depth data such as step length and walking speed.In this study, we demonstrated that both 2D and 3D gait features calculated from video are correlated to clinical measures of parkinsonism severity in gait, as rated on the UPDRS and SAS scales. These findings suggest that both 2D and 3D vision systems have applications in longitudinal monitoring of parkinsonism severity in residential settings.The strength of this study is that we were able to analyze natural walking bouts of individuals with dementia and compare the 2D and 3D gait features associated with parkinsonism. Because both 2D and 3D gait features were calculated for most walking bouts, direct comparison between the association of 2D and 3D gait features and clinical scores was possible. A limitation of this study is that the regression models developed were not well suited for prediction as they could only identify linear relationships between the gait features and clinical scores. Future work will extend the results obtained in this study and focus on developing predictive models for scoring severity of parkinsonism in gait using timeseries data of joint coordinates, without relying on the explicit computation of gait features. It is hypothesized that developing machine learning models that do not rely on handcrafted features will be able to better capture latent structure in the underlying data and thus learn to predict UPDRS-gait and SAS-gait scores more accurately.Additional file 1: Table A. Distribution of UPDRS-gait and SAS-gait scores by participant for walks with 2D gait features. Table B. Distribution of UPDRS-gait and SAS-gait scores by participant for walks with 3D gait features.


This study aimed to compare the shear bond strength (SBS) and adhesive remaining index (ARI) using one conventional and two novel adhesive systems with clinical step reduction and direct and indirect bonding. A sample of 72 human premolars were divided into six groups of 12 samples. The first three groups  were bonded with a direct technique, while the remaining groups  were bonded by the indirect technique. Groups G1 and G4 used conventional acid-etching primer composite (XT); groups G2 and G5 used self-etching bonding (BO), and groups G3 and G6 had an acid-etching treatment followed by a self-adhesive composite (OC). All groups were exposed to thermocycling. Shear bond strength was analyzed with a universal test machine, and the ARI was examined with 4× magnification. The results showed statistically significant differences between the three adhesive systems. The highest strength values were observed in the XT group G1 (13.54 ± 4 MPa), while the lowest were shown in the BO G2 samples (5.05 ± 2 MPa). There was no significant difference between the direct or indirect bonding techniques on the three compared groups. The type of primer and bonding material significantly influenced the SBS. Values with self-etching bonding were below the minimum recommended for clinical use (5.9–7.8 MPa). There was no difference between indirect and direct bonding techniques. The lowest ARI scores (0–1) were observed in both self-etching and BO groups. Further clinical studies are needed to compare in vivo results. Since the introduction of the first bonding systems, there has been a constant effort to improve the quality of materials . ResearcConventional adhesive systems use three different agents during the bonding process: an enamel conditioner, a primer solution, and an adhesive resin. Phosphoric acid has remained the primary enamel etchant since its introduction by Buonocore , despiteIndirect bonding (IB) has emerged in recent years as the best option to achieve a precise bracket placement. The technique introduced was by Silverman , and modClassification of samples according to the adhesive remnant index (ARI) reveals information about the bracket bonding enamel interface and has great value concerning the enamel condition after debonding. Lower values tend to show less damage on the surface of the enamel and are rarely related to enamel fractures, but can be related to insufficient bond strength. On the contrary, higher ARI values are shown in efficient unions between adhesive and enamel, but are often associated with greater surface damage .A novel bonding protocol involving new adhesive systems that reduce steps during bonding, such as self-etching or self-adhesive methods, is of great clinical interest among orthodontists. It would also be of interest to incorporate the indirect bonding technique, given its previously mentioned advantages. Therefore, the aim of this in vitro study was to evaluate the efficiency of three types of bonding systems: a conventional acid-etching composite, a self-etching adhesive method, and a new self-adhesive composite, in combination with two bonding techniques (direct and indirect bonding). The efficiency of the systems was evaluated by computation of the shear bond strength (SBS) and the adhesive remnant index (ARI), aiming to obtain information that helps to develop the best adhesion protocol for further clinical studies.A preliminary pilot study was conducted to test the methodology. After pilot study analysis, sample size calculation was set to achieve a statistical power of 80% with a significance level of 5%, taking into account a 2.5 standard deviation. A sample of 12 premolars per group was estimated. With this data, a study protocol was developed and approved by the ethics committee at the Universitat Internacional de Catalunya .Seventy-two freshly extracted premolars showing no decay or fractures were introduced into 0.9% physiological saline and stored at 37 °C. The three tested bonding materials are shown in All samples were mounted with a custom-made jig, as described by Flores et al. , to stanGroup 1: TX/Direct BondingThe teeth were pre-treated with 37% orthophosphoric acid for 30 s and irrigated with water for 10 s before the adhesion procedure of the brackets. The bonding protocol was followed according to the manufacturer’s instructions. Proper drying of the enamel after etching was performed, followed by the application of a thin layer of Transbond XT primer  on the tooth surface. Then, a small amount of composite was dispensed in the mesh, and bracket positioning was undertaken with a constant low pressure and removal of excess material. Finally, it was polymerized for 30 s  with a LED lamp .Group 2: BO/Direct bondingIn this group, we used a self-etching composite based on a single bottle primer presentation and the composite Beauty Ortho Bond II . It enabled us to do the priming and etching in a single step, following the manufacturer’s instructions. The self-etching primer was applied on the enamel surface by rubbing for 3 s without previous acid-etching. Then, the surface was dried with low pressure, and the bracket was placed using the Beauty Ortho Bond II light-cured composite by direct bonding. The photopolymerization was done in the same way as that in group 1.Group 3: OC/Direct BondingThe teeth were pre-treated with 37% orthophosphoric acid for 30 s and irrigated with water for 10 s before the adhesion procedure of the brackets. Proper drying at a low speed was carried out, and we proceeded to spread the self-adhering composite GC Ortho Connect onto the bracket mesh. The positioning of the bracket was carried out with a low and constant pressure and posterior removal of excess material. Finally, we proceeded with photopolymerization, using an LED light  for 30 s .Group 4: TX/Indirect bondingFor the indirect bonding, we obtained cast models as previously described, in which marks were made for the positioning of the bracket. The bracket was then positioned with Transbond XT and LED light-curing of 20 s per bracket. Once the brackets were placed in the cast, we continued the fabrication of the transference tray with Elite Glass  that was applied to the cast by a mixing pistol in a uniform way over the teeth with a work time of 40 s and 2 min 15 s of setting time in a 23 °C temperature. Once we had obtained the transference tray, we removed it from the cast with the bracket and proceeded with the sandblasting.For the treatment of the natural teeth, 37% orthophosphoric acid was applied for 30 s, posterior irrigation with water occurred for 10 s, the drying of the surface was carried out as in previous groups, and the application of the primer followed the manufacturing instructions. We then placed a fine layer of composite in the bracket mesh and made the transference tray with light-curing for 30 s .Group 5: BO/Indirect BondingIn this group, we applied the same protocol to natural teeth as in group 2, and the transference tray was made as described in group 4.Group 6: OC/indirect groupIn this group, we applied the same protocol to natural teeth as in group 3, and the transference procedure was the same as described in group 4.The tooth samples were stored with hydration in a physiological saline solution of 0.9% at 37 °C. All groups were exposed to thermocycling before debonding 24 h after bracket placement. The thermocycling consisted of 1500 cycles in a water bath at 5 and 55 °C, with bath times at 1 min intervals.2).We tested the samples of all the different groups with a universal testing machine  at a maximum speed of 0.1 mm/min . The resExamples of adhesive remnant index (ARI) classification and values are shown in Numerical variables were described with mean and standard deviation, while the median was described with minimum and maximum values. Categorical variables were described with frequencies and percentages.Differences of SBS between the groups of the study were analyzed with one-way ANOVA, and the Shapiro–Wilk test was used to check the normality of residuals. In addition, a comparative post hoc test was used with Bonferroni correction.The Kruskal–Wallis and Mann–Whitney tests were used to analyze differences in the distributions of the ARI scores.The samples were classified based on a visual scale by one operator (A.I.), and intra-operator analysis was performed for the ARI values with a regression line of all the samples. The validity of the classification was confirmed with an 0.85 correlation coefficient.p < 0.05.Results were analyzed with a statistical software program . The level of statistical significance was set at A description of the results of the shear bond strength is shown in p < 0.001), and we checked the normality residuals with the Shapiro–Wilk test (p = 0.103).One-way ANOVA showed significant effects of groups (The difference between groups and multiple comparisons by t-test with Bonferroni correction are shown in p < 0.001). Thus, a subsequent Mann–Whitney test with Bonferroni correction was performed to analyze the differences between them  was observed in Group 5.This investigation intended to analyze whether the reduction of clinical steps in the two bonding materials had an influence on SBS results when compared to the conventional etch bonding resin procedure in both direct and indirect bonding procedures. Moreover, we also compared how ARI can vary with the use of different bonding systems and bonding techniques to evaluate enamel conditions and make assumptions about which step of the bonding procedure may cause failures in adhesion.The limitations of any in vitro study includes variables of the intra-oral environment such as saliva contamination, enamel composition, or occlusal forces, that greatly influence adhesion and can alter the results of similar in vivo studies. We tried to mitigate these differences by thermocycling all samples up to 1500 cycles . Even soAll 72 premolars were randomly allocated to the six groups of study. Even though we were not able to perform intra-operator tests for the SBS trials, all debonding mechanical tests were performed by an experienced orthodontist in this field (A.I.) and supervised by a senior researcher in bonding materials (T.F.). Premolars are the most commonly used teeth for this kind of study as their availability is not in conflict with the ethics committee due to frequent extraction for therapeutic orthodontic reasons. In addition, it is advisable to use the same teeth, since the area of the bracket mesh is necessary to obtain the MPa conversion, and having several types of bracket widths could be a variable to take into account.We compared three different bonding materials in which the conventional acid-etching primer composite (XT) served as a control group to compare the two other novel bonding types (BO and OC). As far as we know, the self-etching system used in our study has not been commercialized and was loaned for testing in the university without any commercial interest. At the time of this study, there was no literature concerning the self-adhering system we used either. From the clinical point of view, any reduction of steps represents an improvement of the technique, reducing potential mistakes by the operator between steps, and reducing chair time. For this reason, we found the new bonding materials to be of interest for this research.The indirect bonding technique emerged as an alternative to reduce operator stress during chair time and achieve a more accurate placement of the bracket from the beginning of the treatment ,30,31 ReThe bond strength of orthodontic appliances must be able to withstand intra-oral forces during treatment. An optimal adhesion with SBS minimum values of 5.9–7.8 MPa  must be The self-adhering resin simplifies the bonding process by reducing it to a one-step procedure after enamel etching. Previous studies with self-adhering flowable resins stated significantly lower SBS values when compared with conventional systems ,46,47. TData about failure at the bracket-adhesive or the adhesive-enamel interface has both advantages and disadvantages. The tested samples revealed significantly lower values of ARI in both BO groups, in agreement with previous studies ,12,32. TAll self-etching samples showed significantly lower shear bond strength values when compared with the acid-etching groups. BO samples showed values below what is recommended for clinical use (5.9–7.8 MPa).The self-adhesive composite groups demonstrated similar values to those of the XT that acted as a control group, illustrating that this material had efficient forces, regardless of the reduction of one clinical step.There were no statistical differences between the indirect bonding technique groups when compared with their direct bonding analogs. Therefore, indirect bonding technique is a valid alternative bonding procedure.ARI scores suggested that the acid-etching samples (with the majority of scores between 2 and 3) created an effective bonding-enamel attachment but could lead to further enamel damage during the polishing procedure.Further clinical studies with the same materials are necessary.


Smoldering multiple myeloma (SMM) is an asymptomatic precursor state of multiple myeloma (MM). Recently, MM was redefined to include biomarkers predicting a high risk of progression from SMM, thus necessitating a redefinition of SMM and its risk stratification. We assembled a large cohort of SMM patients meeting the revised IMWG criteria to develop a new risk stratification system. We included 1996 patients, and using stepwise selection and multivariable analysis, we identified three independent factors predicting progression risk at 2 years: serum M-protein >2 g/dL (HR: 2.1), involved to uninvolved free light-chain ratio >20 (HR: 2.7), and marrow plasma cell infiltration >20% (HR: 2.4). This translates into 3 categories with increasing 2-year progression risk: 6% for low risk ; 18% for intermediate risk , and 44% for high risk . Addition of cytogenetic abnormalities , t, +1q, and/or del13q) allowed separation into 4 groups  with 6, 23, 46, and 63% risk of progression in 2 years, respectively. The 2/20/20 risk stratification model can be easily implemented to identify high-risk SMM for clinical research and routine practice and will be widely applicable. Before International Myeloma Working Group (IMWG) revised the MM diagnostic criteria, SMM was defined by the presence of either a serum monoclonal protein of ≥3 g/dL (or ≥500 mg/24 h in urine or both) and/or ≥10% bone marrow plasma cells (BMPCs) without evidence of any CRAB symptoms . SMM is a heterogeneous disease with a risk of progression to MM of 10% per year during the first 5 years following diagnosis, decreasing to 3% per year over the subsequent 5 years, and 1% per year after the 10 years of diagnosis3. The Mayo Clinic and the Spanish Group had previously created different risk stratification models that can identify SMM patients with a 2-year risk of progression to MM of ≥50%. The Mayo Clinic model included M-protein (≥3 g/dL), BMPCs infiltration (≥10%), and the ratio of serum free light chain (sFLC) (≥8 or <0.125) to categorize patients into three subgroups, with a 52% risk of progression in 2 years for those presenting with all three risk factors4. The Spanish model used the presence of ≥95% clonal plasma cells among all BMPCs by immunophenotyping and the presence of immune paresis and identifies a subgroup of patients having both features with a 50% risk of progression at 2 years5.Smoldering multiple myeloma (SMM) represents a transitional stage between monoclonal gammopathy of undetermined significance (MGUS) and active multiple myeloma (MM)6. The Eastern Cooperative Oncology Group (ECOG) group has also reported a significant benefit for the early treatment with single-agent lenalidomide versus observation in SMM7.The standard of care for SMM, irrespective of the risk status of the patients, has been observation. However, there are two randomized studies confirming the benefit of early treatment in reducing the risk of progression to MM. The Spanish Myeloma Group conducted the first phase III trial in high-risk SMM, comparing lenalidomide plus dexamethasone (Ld) versus observation, and Ld significantly delayed the progression to MM and patients lived longer )8.Although the Spanish trial did not lead to a change in the standard of care, it encouraged the IMWG to revise the criteria for the definition of SMM and MM to identify a subset of SMM with a 2-year risk of progression of approximately 80% who were then categorized as MM. The identification of this subgroup is based on the presence of any one of these biomarkers: BMPCs ≥60% or involved/uninvolved sFLC ratio ≥100, and presence of >1 focal lesion on magnetic resonance imaging, in patients having bone marrow with at least 10% of clonal BMPC infiltration. This led to a paradigm shift as these patients were then regarded as having myeloma and were offered therapy14. At the same time, many clinical trials are ongoing in high-risk SMM according to different risk models, and all these developments brought to the fore two issues: (i) the heterogeneity of the different risk models developed using rather small series of SMM patients and (ii) the real impact of the revised SMM definition. This forms the basis of the current study.Other groups, in parallel, also created different models to identify SMM patients with a progression risk at 2 years of 50% using different features like positive uptake on positron emission tomography–computed tomography (PET-CT), type of M-protein, cytogenetic abnormalities, increase in serum M-protein and decrease in hemoglobin count over time, presence of Bence Jones proteinuria, or genetic signatures, among othersWe studied a large cohort of SMM patients to identify factors that predicted progression to MM with the goal of developing an easy risk score to predict the 2-year progression risk. The factors identified were M-protein (>2 g/dL), BMPCs infiltration (>20%), and the ratio of involved versus uninvolved sFLC (>20). In addition, we have also created a risk score that accounts for the entire range of the variable measurements, which can be used to provide a more individualized 2-year risk of progression for every SMM patient.8. Patients included in this analysis required baseline data from diagnosis (+/−3 months), no progression to MM or other plasma cell disorders within 6 months from diagnosis, a minimum follow-up of 1 year, and should not have been included in any therapeutic trial.Patients with SMM diagnosed after January 2004 were included in this retrospective medical chart review from participating IMWG sites globally. The SMM definition required to be included in the study was based on the 2014 IMWG criteriaTrained staff collected data from the patient medical charts in three case report forms: (i) patient registration and demographics form, (ii) diagnosis and baseline assessment form, and (iii) 12-month assessment form. The Institutional Review Board at each site approved the study. The study was conducted in accordance with the Declaration of Helsinki and the International Council for Harmonisation Guidelines.The primary objective was to develop a risk stratification model that will identify SMM patients who have high risk of progression to MM or other plasma cell disorders (50% progression risk within the first 2 years from diagnosis) based on the 2014 IMWG criteria for definition of both SMM and MM.Time to progression (TTP) to MM or amyloidosis was the primary end point and was defined as the time elapsed between diagnosis of SMM and when the patient experienced progressive disease. All patients who did not progress at the time of last follow-up were censored in the TTP analysis. A secondary objective was to create a risk scoring tool that would allow individualized estimate of risk at various time points from diagnosis.p ≤ 0.25, optimal cut points were identified using Youden’s Index. Receiver operating characteristic (ROC) curves were generated, with the cut-off maximizing both sensitivity and specificity determined. Sensitivity and specificity were reported for these optimal cut points. Interaction term between candidate variables were included in the model selection if candidate variables with correlations satisfy the above criteria. Using the optimal cut points, a stepwise regression analysis was used to determine predictive variables using a required selection stay criterion of 0.001. A random forest algorithm consisting of an ensemble of decisions trees15 created using the random Forest package was used to confirm variable selection16. Important prognostic variables for predicting probability of progression at 2 years were identified and ranked based on the percentage change in mean squared error. Similar methods were used to identify the most significant cytogenetic abnormalities associated with progression to MM in patients with available information.Due to the nature of the study, no formal hypothesis was used to calculate the sample size. We summarized categorical variables as proportions and continuous variables as medians (range). Univariate Cox regressions were run for each factor to assess an initial relationship between a factor and progression to MM or related disorders. For each factor where Patients were categorized based on the number of risk categories present and Cox proportional hazard regression models were used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs). Kaplan–Meier curves were used to illustrate TTP across the number of risk categories present.17. Variables were categorized based on clinically relevant as well as spline functions to establish the general trends in the risk of progression with increasing laboratory values. Scores for each risk factor were assigned relative weights of each coefficient in the multivariable regression model. Predicted risk of progression were summarized based on this scoring tool. Analyses were conducted in SAS version 9.4  and R Core Team (2018).A risk score was developed to elucidate the potential strength of individual relationships between each of the previously defined variables based on the method described by Sullivan et al.One thousand nine hundred and ninety-six SMM patients from 75 centers in 23 countries were included. Baseline characteristics are in Table The median follow-up from diagnosis was 3.0 years IQR 1.6–5.1). At the data cut-off, 815 (41%) patients had progressed to MM or a related disorder. The median TTP for the entire cohort was 6.4 years (95% CI 6.0–7.2); the 2-, 5-, and 10-year risk of progression were 22, 42, and 64%, respectively .The factors initially included in the univariate analysis for predicting progression risk at 2 years from the diagnosis are shown in Table From among these, the stepwise model selection and random forest algorithm identified serum M-protein concentration, involved to uninvolved sFLC ratio, and BMPC percentage as the most relevant factors predicting progression to MM, and based on Youden’s Index in the ROC analyses, the optimal cut-offs for the risk factors were 1.9 g/dL for serum M-protein (specificity 60% and sensitivity 71%), 19.3 for the involved to uninvolved sFLC ratio (specificity 79% and sensitivity 55%), and 16.4% for the BMPC infiltration (specificity 55% and sensitivity 75%) as shown in Table We then proceeded to create a risk stratification model including 1363 patients in whom all three factors were available: five hundred and twenty-two patients (38%) did not present any of the factors (reference group) with a risk of progression at 2 years of 6% (the low-risk group); 1 out of the 3 factors was identified in 445 patients (33%) with a 18% risk of progression to MM at 2 years  (the intermediate-risk group); and the high-risk group defined by the presence of 2 or 3 risk factors included 396 patients (29%) with a 44% progression risk at 2 years  Fig. . Of the n = 225; 33%) and defined by the presence of none of the factors; low–intermediate  if one factor was present and the progression risk at 2 years was 23% ; intermediate risk  defined by the presence of 2 factors and risk of progression at 2 years of 46% ; and the high risk  with a progression risk at 2 years of 63%  if ≥3 of the factors were present.We explored the added value of the presence of any cytogenetic abnormalities to the 2/20/20 risk model if all factors were available (689 patients). The stepwise model selection identified the presence of t, t, +1q, and del13q/monosomy 13 by fluorescence in situ hybridization (FISH) as the most relevant ones. This model defined four groups of SMM patients with different progression risk at 2 years  together with the cytogenetic abnormalities were included in a logistic regression model for creating a scoring tool to provide a more individualized assessment of risk. This approach allowed us to utilize the variables along the entire range of values instead of using single cut points. Six hundred and eighty-nine patients were included and Tables 19. The mostly incurable nature of MM and a better understanding of the disease biology coupled with the prolonged precursor phase has inevitably directed our attention to developing strategies for prevention or early intervention with the goal of delaying onset of active disease or potentially eradicating the clone21. The redefinition of active myeloma was a first step in early intervention, with treatment initiation not solely based on CRAB criteria. The current study fills an important gap in terms of a risk stratification approach for the newly defined SMM that will allow us to design future clinical trials. We present here a simple risk stratification model that can be applied across the globe using easily available data to identify a subgroup of SMM patients with 50% progression risk at 2 years, defined by the presence of two or three factors among M-protein >2 g/dL, BMPCs infiltration >20%, or the ratio of involved to uninvolved sFLC >20. In this study, we used 2-year progression as the end point in order to maintain the approach used in the previous models but now excluding the ultrahigh risk patients with ≥80% risk of progression at 2 years who are essentially active M by current definition. This group represents a relevant population for early intervention and inclusion in trials in order to either prevent the MM development or potentially cure the disease.The treatment paradigms for myeloma has evolved rapidly in the past decade with introduction of new therapies, leading to improved survival22. Importantly, this international database of nearly 2000 SMM patients confirmed the Mayo Clinic 2/20/20 model, providing robust validation for this new categorization of SMM patients. In addition, the model is based on three features easily available at most centers. Moreover, a subanalysis conducted in a subset of patients with FISH information showed that the presence of any one of the cytogenetic abnormalities such as t, t, +1q, or del13q/monosomy 13 was an additional predictor of progression in this study. This is in line with a previous observation from the Mayo Clinic study that identified the presence of t, del17p), and/or hyperdiploidy as predictors of progression10. Altogether these data reinforce the concept that the genomic information such as mutations and translocation affecting MYC may contribute to refine the model, but this will require prospective validation in other studies23.The updated IMWG definition segregated from the classical SMM definition those patients with an imminent risk of progression to MM; now considered as MM. This subset of patients could potentially overestimate the progression risk in the classical SMM series and should therefore be excluded from the SMM risk stratification models. The Mayo Clinic group recently examined their cohort of SMM patients by removing those with MM according to the 2014 IMWG criteria, and they identified SMM at 50% risk of progression at 2 years based on M-protein (>2 g/dL), BMPC infiltration (>20%), and the ratio of involved to uninvolved sFLC (>20)Different investigators have defined alternative models to evaluate the risk of progression from SMM to MM. Most have been based on measurements of tumor bulk: size of M-spike, sFLCs, type of immunoglobulin IgA versus IgG, proportion of clonal plasma cells >95% of aberrant cells, circulating plasma cells, or immune paresis. These models are useful and have driven the clinical research so far, being validated in clinical trials. However, new models have emerged based on relatively small series of patients utilizing new imaging assessments like PET-CT, genetic signatures, Bence Jones proteinuria, or dynamic models such as the evolution of the M-component and the decrease of hemoglobin, among others. Although many of them identify SMM patients with ≥50% risk of progression to MM within the first 2 years since diagnosis, in clinical practice, physicians are frequently confused about what model to use to define the risk of progression in SMM; moreover, many ongoing clinical trials in SMM use different inclusion criteria, which may be a confounding factor upon analyzing the efficacy of new drugs/combinations in this setting. Taken together, this model derived from an international SMM population with commonly available and reproducible biomarkers could be employed as a standard in registration trials as well as routine clinical practice.7. Moreover, the same group decided to amend their new phase 3 trial comparing Ld versus Ld plus daratumumab in high-risk SMM in order to introduce the 2/20/20 model as inclusion criteria. The ASCENT trial conducted by the International Myeloma Foundation with a curative strategy is using the same model.The phase 3 trial conducted by the ECOG group evaluating single-agent lenalidomide versus observation in SMM patients showed a significant benefit in progression-free survival for the high-risk subset defined as in the current study using the 2/20/20 modelWhile we used 2-year progression as the end point to define a high-risk SMM population, we have also created a more precise and individualized scoring tool to classify individuals by risk of progression using the entire spectrum of values for each patient (in place of dichotomous division) including M-spike, BMPC infiltration, and sFLC ratio. Accordingly, this scoring tool is able to precisely identify SMM patients with extremely low risk of progression at 2 years (close to MGUS), as well as SMM with a risk of progression at 2 years even >50%. Thus, using this risk scoring, SMM patients with total risk score of 1 have 90% of probability of not developing MM in 2 years  = 90%), while for those patients with total risk score of 9, the probability of developing MM in 2 years will be of 93% . However, the identification of SMM patients who will not progress with near certainty (100% NPV) is difficult, and in the subgroup of patients with total risk score of 0, the risk does exist.24. Moreover, the transition process from SMM to MM could also involve growth of preexisting clones due to a more permissive bone marrow microenvironment26. The current study also does not factor in other demographic factors such as race as the numbers were insufficient to explore this.There are some limitations in this study because of its retrospective nature as well as the missing data observed for some variables that may have led to their exclusion in the multistep process. This is the case for the presence of immune paresis, percentage of plasma cells with aberrant phenotype or circulating plasma cells, or the evolution of the M-component and the decrease in hemoglobin. In addition, differences in the specific methodology used for FISH may vary from institution to institution. One additional limitation is its complete reliance on clinical features. It has been recently shown that the mutational landscape, particularly mutations in the RAS family as well as c-Myc alterations, may independently predict progression riskIn summary, our study identifies a subgroup of SMM patients with 50% progression risk at 2 years from diagnosis based on the presence of two or three factors among M-protein (>2 g/dL), BMPC infiltration (>20%), or the ratio of involved versus uninvolved sFLC (>20). This model is easily reproducible and available worldwide, could be used to identify high-risk SMM patients in the context of clinical research, and will contribute in the near future to be able to offer early treatment to a more homogeneous subgroup of SMM patients. Availability of FISH data can improve the model and should be considered in all patients diagnosed with SMM. In addition, development of a scoring system allows for more individualized risk assessment. The trials going on and incorporating these features for the selection of the high-risk SMM patients will contribute to validate the model and ancillary biological studies can help to optimize it in the future. Future studies should focus on incorporating other tumor cell characteristics such as the presence of mutations as well as alterations in the tumor microenvironment, especially changes in the immune parameters.