Smoking prevalence remains high in Malaysia. Primary care doctors have a good opportunity to motivate the smokers to quit smoking in view of the accessibility of primary healthcare clinics to the public. The objective of this study was to determine the practice of smoking cessation management among primary care doctors and its associated factors.A cross-sectional online survey was carried out among 383 medical officers and interns in all government primary healthcare clinics in the district of Petaling, Klang and Hulu Langat from June to August 2020. All doctors were involved in the care of patients for smoking cessation. The knowledge, attitude and practice of smoking cessation management were assessed using a 17-items validated questionnaire which covered the components of 5As  and 5Rs . The management of pre-contemplation phase included the components of ask, advise, assess and 5Rs. The management of the contemplation phase included the components of assist and arrange.The majority of the respondents had poor score of knowledge (62.4%); attitude (58%) and practice . Using multivariate logistic regression analysis, the significant factors associated with the poor practice of smoking cessation management in the pre-contemplation phase were poor  or moderate knowledge , poor attitude , lacks smoking cessation banners, brochures and leaflets in the clinic  and lack of nicotine replacement medications . No significant factors were shown associated with the practice of the contemplation phase.The majority of primary care doctors had poor knowledge, attitude and practice of smoking cessation management. Factors that had increased the odds of the poor practice of smoking management at the pre-contemplation phase were poor knowledge, poor attitude, and insufficient organizational support for health promotion materials and nicotine replacement medication. Smoking carries high mortality and causes approximately 90% of all lung cancer deaths, 80% of all chronic obstructive pulmonary disease deaths and increases the risk for death from all causes in men and women worldwide . In MalaFor curbing tobacco use in Malaysia, apart from supporting the World Health Organization Framework Convention on Tobacco Control and implementing legislation on local tobacco production via Control for Tobacco Products Regulation, Malaysia had taken the initiative to set up smoking cessation services (mQuit services) in most government primary healthcare clinics and hospitals , 5. The The health care system in Malaysia is provided by both the public and private health sectors, and the Ministry of Health being the major provider for the public sector , 8. The Studies had shown that advice from health professionals was effective in increasing cessation, primarily through aiding and motivating them to make a quit attempt . It was A cross-sectional study was conducted in all government primary healthcare clinics in the district of Hulu Langat, Klang and Petaling in the state of Selangor. These three districts were purposely selected in view of the high density of the population and the large number of doctors served in these areas. There was a total of 32 government primary healthcare clinics comprised of 223 doctors in the district of Hulu Langat, 132 doctors in the district of Klang and 198 in the district of Petaling. The study was conducted from June to August 2020. All the doctors were involved in identifying and counselling smokers to quit smoking during their daily consultation and recruiting those in the contemplation phase to quit smoking clinic.All primary care doctors consisted of the medical officers and interns working at the government primary healthcare clinics in the district of Hulu Langat, Klang and Petaling were invited to participate in the study. Medical officers are doctors who had passed two years of internship training and are involved in the clinical management of patients in primary healthcare clinics while interns are medical graduates who are still undergoing internship training under the supervision of a specialist. Those on long leave for more than one month were excluded.The questionnaire was an online questionnaire refer . AnonymoThis validated questionnaire consisted of four component  . There wThe assessment of the knowledge component was on the 5A components  . The twoThe attitude component was assessed according to the doctors’ perception on the relevancy of clinical practice guidelines in improving smoking cessation, the worthiness of putting effort in helping smokers to quit smoking and the benefit of repetition in giving advice to assist patients in smoking cessation . It was The practice component was assessed according to the stage of change: the precontemplation and contemplation phase . The praEthical approval for this study was obtained from the Medical Research and Ethics Committee (MREC), Ministry of Health Malaysia (NMRR-19-2175-48947).The data was undertaken using the IBM SPSS statistic version 26.0. There were two outcomes in this study: the components of the practice of smoking cessation management in the pre-contemplation phase and the practice of smoking cessation management in contemplation phase. The median score of these outcome variables was reported as the data was not normally distributed. The practices were reclassified into two groups, the good practice group for those who scored higher than the median score and the poor practice group for those who scored equal to or less than the median score.The total score for knowledge was 2. It was categorized as poor knowledge (score-0), average knowledge (score-1) and good knowledge (score-2). The attitude score ranged from minimum score of 0 to maximum score of 6. Those scored higher than median score was classified as good attitude and those scored equal or less than the median score was classified as poor attitude.To examine the associated factors with the practice of smoking cessation assessment and management at the pre-contemplation and contemplation phase, Pearson Chi-Square/Fisher exact test was used for bivariate analysis and multiple logistic regression was used for multivariate analysis. From the bivariate analysis, factors with p-values equal to or less than 0.25 were included in the multiple logistic regression.Testing for multicollinearity, assumptions and outliers was also carried out before multiple logistic regression analysis. Testing for multicollinearity of the independent variables was carried out by examining the variance inflation factor (VIF). There was no multicollinearity detected and the VIF ranged from 1.01 to 6.88. The tolerance level of 0.1 (= VIF 10) was used. The statistical significance in the final model was accepted at p-values equal to or less than 0.05. The model fitness was assessed using the Hosmer-Lemeshow goodness of fit test. The analysis with Hosmer-Lemeshow test showed a p-value of more than 0.05, indicating an adequate model fit. The outliers were checked using the Cook’s distance, leverage value, studentized and standardized residual and the values were within the acceptable limit.The response rate was 69.2% (383/553). Most of the participants (56%) were aged 31 to 35 years, and the median age was 33 (IQR 4) years. The majority of the respondents (71.5%) was female. More than half (56.1%) of the respondent had 6 to 10 years of service experience, and the median years of service was 8 (IQR: 5) years. The majority of the respondents were medical officers (98.4%), and reported that they had never smoked (96.9%) Refer .The majority of the respondents (92.2%) had encountered smokers during consultation in the past one month, and 76.2% of these respondents who had encountered smokers reported that those smokers were willing to quit smoking. The majority 84.9% (325/383) of the primary health care doctors reported a designated smoking cessation clinic at their clinic, and 87.2% (334/383) reported that they had attended smoking cessation courses.Meanwhile, 64.2% (246/383) of primary health care doctors reported the unavailability of smoking cessation banners, brochures and leaflets and 78.1% (299/383) reported the unavailability of nicotine replacement medications.The median score for the attitude was 2 (IQR 2) and practice for the pre-contemplation and contemplation phase was 8 (IQR 5) and 2 (IQR 1) respectively. More than half of the doctors attained poor scores in the components s of knowledge, attitude and practice. There was a preponderance of 62.4% (239/383) of doctors with a poor knowledge score of 0. Most doctors (58%) had a poor attitude . Half of the doctors (50.9%) reported having poor practice  in the pre-contemplation phase and 75.7% of doctors had a poor practice of smoking cessation assessment and management  in the contemplation phase Refer .For the knowledge component, more than half of doctors (62.4%) attained the wrong answer for both questions. 77.5% and 68.4% of the participants attained wrong answers for the ‘assess’ and ‘assign’ components of smoking cessation management respectively. In the attitude component, a small proportion (6.5% to 14.4%) of doctors obtained the full score of 2 for all three questions whereby only 6.5% disagreed on their effort in helping smokers to quit was not well rewarded while 14.4% of them disagreed on clinical practice guidelines was not relevant in smoking cessation management. Meanwhile, only 15.4% of the doctors agreed that repetition in advising smokers was beneficial.For the practice of smoking cessation management in the pre-contemplation phase, only about one-fifth (17%) of the doctors always assessed the patients’ status of smoking and about 40% of them always advised patients on quitting smoking. For the practice of smoking cessation management at the contemplation phase, there was only one-tenth of the doctors (13.1%) had always provided smokers with practical counselling. For the 5R components, the result generally showed a poor practice as less than a quarter of doctors had always practised ‘Relevance’ (19.1%); ‘Risk’ (14.1%); ‘Reward’ (15.1%); ‘Road-blocks’ (14.1%) and ‘Repetition’ (26.1%) Refer .Four factors: (1) Organizational support of quit smoking promotion materials  Availability of nicotine replacement medication (3) Knowledge) and (4) Attitude were significantly associated with the practice of smoking cessation management at the pre-contemplation phase refer . PrimaryFor the binary logistic regression analysis of the practice at the contemplation phase of smoking cessation management, there were no significant factors identified refer .We found that the proportion of primary care doctors with poor knowledge, attitude, and practice of smoking cessation assessment and management was high: 62.4% for knowledge, 58% for attitude, 50.9% for practice at pre-contemplation and 75.7% for practice at contemplation phase. Many of them reported the unavailability of smoking cessation banners, brochures, leaflets (64.0%) and nicotine replacement medications (78.0%) in their clinics. Factors significantly associated with the practice of smoking cessation management at the pre-contemplation phase were organizational support of health promotion materials , availability of nicotine replacement medication, knowledge and attitude of primary care doctors.The burden of the smoking problem in Malaysia is high . In our Despite most primary care doctors reported attending training (87.2%) for smoking cessation in our study, more than half (62.4%) of the doctors were found to have poor knowledge. This was consistent across other studies among doctors despite disparate tools and knowledge components being assessed , 18, 19.The majority of primary care doctors (73.8% to 93.3%) in Saudi Arabia had shown a good attitude in smoking cessation assessment and management , 13. SimA local study looking at the primary care doctors’ attitude toward smoking cessation in the state of Pahang also reported a similar finding as ours . HoweverOur study found that the practice of smoking cessation management at the pre-contemplation phase and contemplation phase was poor. Literature had also shown that there was a sub-optimal self-reported practice of smoking cessation management using the 5A model , 13, 14.For the management at the precontemplation phase, our result was consistent with the literature which showed that the best practice was the “Ask” and “Advise” components as compared to the “Assess” component , 18, 19.Our study showed that the practice of smoking cessation management in contemplation phase was poorer than in the pre-contemplation phase. This could be due to the doctors might not encounter many patients who were in the contemplation phase. We could not find any significant factors that contributed to the poor practice during the contemplation phase. Further study is needed to explore the possible barriers to this.Inconsistent with the literature, our study showed that organizational support such as the availability of health promotion materials and nicotine replacement medication were found to be significant factors associated with the practice of quitting smoking management , 16. MorThis study was conducted in government healthcare clinics with a large pool of primary care doctors who served as important health care providers to the majority of the Malaysian population. These findings are most relevant and applicable to the current government primary care clinic settings in Malaysia. However, we only included three districts thus the generalisability of the results needs to be interpreted with caution. The resources in private primary care settings are different from government primary care settings. Thus, the result cannot be applied in private primary care settings. Further studies are needed to explore the challenges faced by private primary care doctors in practising smoking cessation management.The questionnaire was self-administered and relied on participants’ self-reporting answers. As the result, recall and socially desirable bias might happen and there could be a certain degree of over- or under-reporting. The assurance of confidentiality and anonymity of the participants for the survey had been carried out to mitigate such bias. We are unable to determine the good score in advance as lacking report of discriminant validity for the good and poor score. The use of median or mean score might wrongly classify the participants into good attitude or good practice if there were no proficient participants in the study. However, this classification could provide some insight regarding the association between the factors with those in the group of higher and lower scores. The items developed for the assessment of knowledge and attitude components in the questionnaire involved low level of cognitive and affective domains. These limit the validity of the questionnaire in determining the association between knowledge and attitude with the practice of smoking cessation management. The items will need to be revised and strengthened further in future study to improve their validity.The practice of smoking cessation assessment and management was poor among primary care doctors. Poor knowledge and attitude, unavailability of nicotine replacement medication and health promotion materials were significant factors associated with poor practice. The intervention targeted at these factors is needed to improve the practice of primary care doctors in delivering quit smoking services in Malaysia.S1 File(DOCX)Click here for additional data file.S2 File(DOCX)Click here for additional data file. 26 Jul 2022 <div> PONE-D-22-17659 </div>  <div> Factors Influencing the Practice of Smoking Cessation Assessment and Management among Primary Care Doctors (SCAAM-DOC) </div>  <div> PLOS ONEDear Dr.  Beatrice Jee Ngee LingThank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.plosone@plos.org. 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You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: This study explores self-reported practice targeting smoking cessation programmes among primary care physicians in three areas of Malaysia. The areas were selected due to the high density of practices. Of 553 physicians 383 participated, an inclusion rate of appr 69 percent, which is fairly good. The dominating professional group in the material is termed “Medical officer”; the meaning of the term is not explained to the reader. The authors found a lack of knowledge and educational and medical support to assist patients in smoking cessation. They suggest that interventions that target these areas should be provided to doctors in primary care.The manuscript is well written, with findings that may alter governmental strategies for smoking cessation in Malaysia. The authors clearly state that their findings differ from similar studies in other countries, thus findings and suggestions for improvement is, in my opinion, applicable to Malaysian health care primarily.I have just a few comments for clarification and improved readabilityTitleL1-3m Factors Influencing the Practice of Smoking Cessation Assessment and Management among Primary Care Doctors (SCAAM-DOC)Should be more specific:Factors Influencing the Practice of Smoking Cessation Assessment and Management among Primary Care Doctors (SCAAM-DOC) in MalaysiaAbstract:Results and conclusions are consistent with the text in the main draft.L40 Repeatedly the authors start a sentence with “Majority” where I would expect a specific form, such as “The majority…” I suggest that the authors consider re-phrasing. The same is observed in L198, 201, 207.L 43-45 I would suggest that the numbers after comma is restricted to twoL50 Suggested re-phrasing: The knowledge, attitude and practice among primary care doctors in three districts in Malaysia were suboptimalIntroductionNo comments. The aim is fairly good addressed in the results and discussionMaterial and methodsAgain, restrict numbers to two numbers after commaL113 The term “medical officer” and “intern” should be defined and described in more detail. In my country a medical officer is a GP mainly working in an administrative role, supporting the national and regional health authorities, and in many cases not in clinical practice at all.L138 A comma is missing (0800)L139 Please explain in more detail the evidence for the 5A and 5R components and their role in mapping knowledge and attitude towards smoking cessation in clinical practice. This should include in which clinical setting that the tool has been validated.Data analysisI am not a statistician, and not in position to critically review the authors’ choice of analyses. Choosing median and not mean is logical, given that data were not normally distributed. The choice of methods for bivariate and multivariate analyses seems reasonable, but the journal may confer a statistician for a review of this section.L191 In what way was the ROC relevant for the assessment of pre-contemplation and contemplations. Does this mean that the results from pre-contemplation is more reliable? If so, this should be discussed.ResultsL210-212 “Table 2 shows 64% (246/383) of primary …” and “78% (299/383) reported on the unavailability of nicotine replacement medications” How does Table 2 show these results?DiscussionI think the authors address their findings, and the relation to existing literature in a reasonable way. They should emphasize that their findings are most relevant to current Malaysian situation because I don’t think that these results necessarily are applicable to other settings.Strength and limitationsL356 In what way differ “government primary care clinics» from other clinical practices in Malaysia? Does these practices have patients with other sociodemographic characteristics than other practices in the country? If there is a difference, is it relevant to discuss whether different strategies should be applied to the various practices?L357 Again, consider re-phrasing “majority”: “the major health care providers to a majority of the population”ConclusionsNo comments besides that the authors should emphasize that findings and conclusions are most relevant to the Malaysian setting.Reviewer #2: 1. Title: it could be better to mention that the study was conducted in 3 districts of Malaysia.2. It looks like the objectives of the study were 1) to assess knowledge (K), attitude (A) and practice (P) of workers regarding tobacco cessation, and 2) assess the association between P and K,A, other factors.3. There is a need to include the hypothesis that is (are) tested.4. Methods:-What was the study period?-How were the issues of response bias as participants may have selected response reflecting good practice while in reality they do not practice according to how they responded?-Were all doctors  involved in the care of patients for smoking cessation?-How many doctors are in the 3 districts? This should appear in the study setting, or study population section/ paragraph.-Line 130: "This questionnaire consisted of four domains ..." There are 3 Bloom's taxonomy domains of learning not four. So P of doctors were assessed in smokers at pre-contemplation and contemplation (change model).- So, the questionnaire consisted of 17 items: 2 for K, 3 for A and 12 for P (10 pre-contemplation and 2 contemplation phase). However, the authors did not clearly state the score allocated to each score. For instance, it is said the K had 2 and A had 6. Does it mean that each item of A had 2 marks while the K items had 1 mark each?- Use of "norm-referenced score" has limitation. For instance, if there are no proficient participants, some of the participants may be wrongly classify as "good attitude" or "good practice"... It could be better for authors to determine in advance the score that will be classified as "good attitude" or "good practice"...-There is a need to state the cut-off point of VIF that was used: Did they use the tolerance level of 0.1 (=VIF 10) or 0.2 (=VIF 5).-Line 188-194: I am not sure why Goodness-of-fit, the Hosmer-Lemeshow test and area under the receiving operating characteristic (ROC) curve were conducted in the study analysis. Authors need to write this clearly.Results:-Titles of the tables need improvement: They should be written in the way that even if one removes from the text, they make sense.-Table 3: The responses are in Likert scale. Putting seldom and never together is confusing.Discussion:-One of the major issue I observe is that K is assessed using 2 recall questions. Recall is the lowest level of cognitive. Does it mean that if someone can recall 2 statements by answering "true" that person has a "good knowledge" on smoking cessation?-The 2 items to assess cognitive domains are low level of bloom's taxonomy . This limits the assessment of this domain and it is hazardous to conclude that psychomotor in pre-contemplation and contemplation is associated with cognitive in smoking cessation management.-Also, items on attitude enquire the low level of affective which is "Receiving Phenomena" (when using the verb ask). Same as above.********** <font color="black"> 6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.If you choose “no”, your identity will remain anonymous but your review may still be made public.Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy. </font> Yes: Eivind AakhusReviewer #1: Reviewer #2: No**********https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step. </div> While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool,  28 Aug 2022Reviewer #1: This study explores self-reported practice targeting smoking cessation programmes among primary care physicians in three areas of Malaysia. The areas were selected due to the high density of practices. Of 553 physicians 383 participated, an inclusion rate of appr 69 percent, which is fairly good. The dominating professional group in the material is termed “Medical officer”; the meaning of the term is not explained to the reader. The authors found a lack of knowledge and educational and medical support to assist patients in smoking cessation. They suggest that interventions that target these areas should be provided to doctors in primary care.The manuscript is well written, with findings that may alter governmental strategies for smoking cessation in Malaysia. The authors clearly state that their findings differ from similar studies in other countries, thus findings and suggestions for improvement is, in my opinion, applicable to Malaysian health care primarily.I have just a few comments for clarification and improved readabilityThank you for the feedback and constructive comments. We had added the details for explaining the term ‘medical officer’ (L117-120) Medical officers are doctors who had passed two years of internship training and are involved in the clinical management of patients in primary health care clinics while interns are medical graduates who are still undergoing internship training under the supervision of a specialist.2. TitleL1-3m Factors Influencing the Practice of Smoking Cessation Assessment and Management among Primary Care Doctors (SCAAM-DOC)Should be more specific:Factors Influencing the Practice of Smoking Cessation Assessment and Management among Primary Care Doctors (SCAAM-DOC) in MalaysiaThe title has been amended as suggested (L1-3).Factors Influencing the Practice of Smoking Cessation Assessment and Management among Primary Care Doctors (SCAAM-DOC) in Three Districts of Malaysia3.Abstract:Results and conclusions are consistent with the text in the main draft.L40 Repeatedly the authors start a sentence with “Majority” where I would expect a specific form, such as “The majority…” I suggest that the authors consider re-phrasing. The same is observed in L198, 201, 207.Amendment done and adjusted. Specific forms “the majority” had been inserted(L41-42)The majority of the respondents had poor score of knowledge (62.4%); attitude (58%) and practice .(L211-213)Most of the participants (56%) were aged 31 to 35 years, and the median age was 33 (IQR 4) years. The majority of the respondents (71.5%) was female.(L220-222)The majority of the respondents (92.2%) had encountered smokers during consultation in the past one month, and 76.2% of these respondents who had encountered smokers reported that those smokers were willing to quit smoking.L 43-45 I would suggest that the numbers after comma is restricted to twoAmendment done and adjusted throughout the documentL50 Suggested re-phrasing: The knowledge, attitude and practice among primary care doctors in three districts in Malaysia were suboptimalAmendment done and adjusted (L51)The majority of primary care doctors had poor knowledge, attitude and practice of smoking cessation management.4.IntroductionNo comments. The aim is fairly good addressed in the results and discussionThank you.5.Material and methodsAgain, restrict numbers to two numbers after commaAmendments had been done to two numbers after comma throughout the documentL113 The term “medical officer” and “intern” should be defined and described in more detail. In my country a medical officer is a GP mainly working in an administrative role, supporting the national and regional health authorities, and in many cases not in clinical practice at all.We had added the details for explaining the term ‘medical officer’ (L117-120) Medical officers are doctors who had passed two years of internship training and are involved in the clinical management of patients in primary health care clinics while interns are medical graduates who are still undergoing internship training under the supervision of a specialist.L138 A comma is missing (0800)Missing comma 0.80 inserted (L146)L139 Please explain in more detail the evidence for the 5A and 5R components and their role in mapping knowledge and attitude towards smoking cessation in clinical practice. This should include in which clinical setting that the tool has been validated.We have added the details for clarity. Refer (L146-158)This questionnaire was validated among 141 primary care doctors from the government health clinics in three districts in the state of Pahang in Malaysia.[16]The assessment of the knowledge component was on the 5A components .[16] The two questions mainly assessed the primary care doctors’ familiarity with the sequence and components of the 5As. (Refer S1 files) These were assessed with a dichotomous scale consisting of options of ‘True’ and ‘False’.[16] A score of one was given for a correct answer and 0 score for a wrong answer.[16] The attitude component was assessed according to the doctors’ perception on the relevancy of clinical practice guidelines in improving smoking cessation, the worthiness of putting effort in helping smokers to quit smoking and the benefit of repetition in giving advice to assist patients in smoking cessation .[16] Data analysisI am not a statistician, and not in position to critically review the authors’ choice of analyses. Choosing median and not mean is logical, given that data were not normally distributed. The choice of methods for bivariate and multivariate analyses seems reasonable, but the journal may confer a statistician for a review of this section.L191 In what way was the ROC relevant for the assessment of pre-contemplation and contemplations. Does this mean that the results from pre-contemplation is more reliable? If so, this should be discussed.Thank you for the feedbackWe agreed that the ROC was not relevant for this analysis. We had removed it. 6.ResultsL210-212 “Table 2 shows 64% (246/383) of primary …” and “78% (299/383) reported on the unavailability of nicotine replacement medications” How does Table 2 show these results?Sorry. There was a typo error and we have deleted the word ‘table2’. (L225-227)64% (246/383) of primary health care doctors reported the unavailability of smoking cessation banners, brochures and leaflets and 78% (299/383) reported the unavailability of nicotine replacement medications. 7.DiscussionI think the authors address their findings, and the relation to existing literature in a reasonable way. They should emphasize that their findings are most relevant to current Malaysian situation because I don’t think that these results necessarily are applicable to other settings.Thank you for the suggestion. We have emphasized this in the section of strength and limitations. (L 375-378)This study was conducted in government primary care clinics with a large pool of primary care doctors who served as important health care providers to the majority of the Malaysian population. These findings are most relevant and applicable to the current government primary care clinic settings in Malaysia.8.Strength and limitationsL356 In what way differ “government primary care clinics» from other clinical practices in Malaysia? Does these practices have patients with other sociodemographic characteristics than other practices in the country? If there is a difference, is it relevant to discuss whether different strategies should be applied to the various practices?We had added the differences between government and other clinical practices in Malaysia under Introduction (L75-81 )The health care system in Malaysia is provided by both the public and private health sectors, and the Ministry of Health being the major provider for the public sector. The government healthcare clinics are highly subsidized and accessible to the public. The patient only needs to pay RM1 to RM5 (USD 0.30-1.20) for a clinic visit. This fee includes consultation, investigations and medications. The doctors in government healthcare clinics encounter more chronic disease follow-up cases while in private primary care clinics more acute illnesses were seen.[25] We had added the information in the strength and limitation (L379-383)The resources in private primary care are different from government primary care settings. Thus the result cannot be applied in private primary care settings. Further studies are needed to explore the challenges faced by private primary care doctors in practising smoking cessation management.L357 Again, consider re-phrasing “majority”: “the major health care providers to a majority of the population”Amendment done and adjusted (L375-377)This study was conducted in government primary care clinics with a large pool of primary care doctors who served as important health care providers to the majority of the Malaysian population. 9.ConclusionsNo comments besides that the authors should emphasize that findings and conclusions are most relevant to the Malaysian setting. Amendment done and adjusted (L401-405)The practice of smoking cessation assessment and management was poor among primary care doctors. Poor knowledge and attitude, unavailability of nicotine replacement medication and health promotion materials were significant factors associated with poor practice. The intervention targeted at these factors is needed to improve the practice of primary care doctors in delivering quit smoking services in Malaysia. Reviewer #2:4.Title:It could be better to mention that the study was conducted in 3 districts of Malaysia.Thank you for the suggestion. We had amended the title.(L1-3)Factors Influencing the Practice of Smoking Cessation Assessment and Management among Primary Care Doctors (SCAAM-DOC) in Three Districts of Malaysia5. ObjectiveIt looks like the objectives of the study were 1) to assess knowledge (K), attitude (A) and practice (P) of workers regarding tobacco cessation, and 2) assess the association between P and K,A, other factors.The outcome of interest for our study is the practice of smoking cessation management among primary care doctors. Knowledge and attitude are part of the factors that we examined along with other factors such as sociodemographic and the organisation support. Thus, we stated our objective as follows: ‘The objective of this study was to determine the practice of smoking cessation management among primary care doctors and its associated factors.’ (L28-29)5.HypothesisThere is a need to include the hypothesis that is (are) tested.Thank you for the suggestion. We had added the hypothesis. (L94-98)We hypothesized that the prevalence of practice of smoking cessation management would be low among primary care doctors and there was an association between socio-demographic factors, organizational support, level of knowledge, attitude with the practice of assessment and management in smoking cessation in the pre-contemplation and contemplation phase among primary health care doctors. 6.Methods:-What was the study period?We had added the details in the abstract (L31-34 ) and methods (L106-113) ;Abstract (L31-34 )A cross-sectional online survey was carried out among 383 medical officers and interns in all government primary healthcare clinics in the district of Petaling, Klang and Hulu Langat from June to August 2020. All doctors were involved in the care of patients for smoking cessation.Materials and methods (L106-113 )These three districts were purposely selected in view of the high density of the population and the large number of doctors served in these areas. There was a total of 32 government primary healthcare clinics comprised of 223 doctors in the district of Hulu Langat, 132 doctors in the district of Klang and 198 in the district of Petaling. The study was conducted from June to August 2020. -How were the issues of response bias as participants may have selected response reflecting good practice while in reality they do not practice according to how they responded?Thank you for the feedback. We acknowledged this issue and the assurance of confidentiality and anonymity of the participants for the survey had been carried out to mitigate such bias (L385-388 )The questionnaire was self-administered and relied on participants’ self-reporting answers. As the result, recall and socially desirable bias might happen and there could be a certain degree of over- or under-reporting. The assurance of confidentiality and anonymity of the participants for the survey had been carried out to mitigate such bias.Were all doctors involved in the care of patients for smoking cessation?Yes. All doctors are involved in the care of patients for smoking cessation.We had added the information in the section of Study design and Data collection (L110-113)All the doctors were involved in identifying and counselling smokers to quit smoking during their daily consultation and recruiting those in the contemplation phase to quit smoking clinic. -How many doctors are in the 3 districts? This should appear in the study setting, or study population section/ paragraph.Thank you. Information has been added as suggested. (L108-110)There was a total of 32 government primary healthcare clinics comprised of 223 doctors in the district of Hulu Langat, 132 doctors in the district of Klang and 198 in the district of Petaling.-Line 130: "This questionnaire consisted of four domains ..." There are 3 Bloom's taxonomy domains of learning not four. So P of doctors were assessed in smokers at pre-contemplation and contemplation (change model).Sorry for the confusion. We had changed the word ‘domain’ to ‘component’ based on the terms used by the original authors throughout the document. The four components had been developed by the authors include; knowledge, attitude, practice of smoking cessation management in the pre-contemplation and contemplation phase.- So, the questionnaire consisted of 17 items: 2 for K, 3 for A and 12 for P (10 pre-contemplation and 2 contemplation phase). However, the authors did not clearly state the score allocated to each score. For instance, it is said the K had 2 and A had 6. Does it mean that each item of A had 2 marks while the K items had 1 mark each? The details were added as follows:Knowledge component (L149-153)The assessment of the knowledge component was on the 5A components .[16] The two questions mainly assessed the primary care doctors’ familiarity with the sequence and components of the 5As. (Refer S1 files) These were assessed with a dichotomous scale consisting of options of ‘True’ and ‘False’.[16] A score of one was given for a correct answer and 0 score for a wrong answer.[16] Attitude component (L155-164 )The attitude component was assessed according to the doctors’ perception on the relevancy of clinical practice guidelines in improving smoking cessation, the worthiness of putting effort in helping smokers to quit smoking and the benefit of repetition in giving advice to assist patients in smoking cessation .[16] It was assessed according to the respondents’ degree of agreement with a five point Likert scale of ‘Strongly agree’, ‘Agree’, ‘Don’t know’, ‘Disagree’ and ‘Strongly disagree’.[16] The scores were given as ‘Disagree’ = 1 mark, ‘Strongly disagree’ = 2 marks, and other responses  = 0 mark and for the final item about ‘repetition in advising to quit smoking is beneficial’; the reverse coding was applied with the score of ‘Agree’ = 1 mark, ‘Strongly agree’ = 2 marks and other responses  = 0 mark (Refer S1).[16]Practice component (L165-171)The practice component was assessed according to the stage of change: the precontemplation and contemplation phase.[16] The practice of smoking cessation assessment and management for patients at pre-contemplation phase include ‘Ask’, ‘Advice’ and ‘Assess’, followed by the 5R on those who are not ready to quit.[16] The practice of smoking cessation management for those patients at contemplation phase include ‘Assist’ and ‘Arrange’. The items were assessed using a four point Likert scale with the score of ‘Always’ = 2 marks, ‘Frequent= 1 mark, other responses (seldom and never) = 0 mark.[16] - Use of "norm-referenced score" has limitation. For instance, if there are no proficient participants, some of the participants may be wrongly classify as "good attitude" or "good practice"... It could be better for authors to determine in advance the score that will be classified as "good attitude" or "good practice"...We acknowledge the limitations of ‘norm reference score’. However, we were not able to determine in advance the score as there was no report on discriminant validity for this scoring. We have added this in the limitations. (L386-390)We are unable to determine the good score in advance as lacking report of discriminant validity for the good and poor score. The use of median or mean score might wrongly classify the participants into good attitude or good practice if there were no proficient participants in the study. However, this classification could provide some insight regarding the association between the factors with those in the group of higher and lower scores. -There is a need to state the cut-off point of VIF that was used: Did they use the tolerance level of 0.1 (=VIF 10) or 0.2 (=VIF 5).(L201-203)There was no multicollinearity detected and the VIF ranged from 1.01 to 6.88. The tolerance level of 0.1 (=VIF 10) was used. -Line 188-194: I am not sure why Goodness-of-fit, the Hosmer-Lemeshow test and area under the receiving operating characteristic (ROC) curve were conducted in the study analysis. Authors need to write this clearly.We had added the information on Hoesmer -Lemeshow test for clarity and removed the ROC curve based on reviewer 1’s comments. (L204-208)The model fitness was assessed using the Hosmer-Lemeshow goodness of fit test. The analysis with Hosmer-Lemeshow test showed a p-value of more than 0.05, indicating an adequate model fit. 7.Results-Titles of the tables need improvement: They should be written in the way that even if one removes from the text, they make sense.We have edited the titles of Table 2 and 3 to improve clarity(L237-239)Table 2. The level of knowledge, attitude and practice of smoking cessation management among primary care doctors(L258-259)Table 3 The likeliness of practice among primary care doctors for the items of smoking cessation management in the pre-contemplation and contemplation phase-Table 3: The responses are in Likert scale. Putting seldom and never together is confusing.Thank you for the feedback. We had separated those answered ‘seldom’ and ‘never’ to improve clarity. (Page L258-259)8.Discussion-One of the major issue I observe is that K is assessed using 2 recall questions. Recall is the lowest level of cognitive. Does it mean that if someone can recall 2 statements by answering "true" that person has a "good knowledge" on smoking cessation?-The 2 items to assess cognitive domains are low level of bloom's taxonomy . This limits the assessment of this domain and it is hazardous to conclude that psychomotor in pre-contemplation and contemplation is associated with cognitive in smoking cessation management.-Also, items on attitude enquire the low level of affective which is "Receiving Phenomena" (when using the verb ask). Same as above.Thank you for the constructive feedback. We acknowledge the limitations of the questionnaires and address this in the limitation. (L393-398)The items developed for the assessment of knowledge and attitude components in the questionnaire involved low level of cognitive and affective domains. These limit the validity of the questionnaire in determining the association between knowledge and attitude with the practice of smoking cessation management. The items will need to be revised and strengthened further in future study to improve their validity.AttachmentResponse to Reviewers.docxSubmitted filename: Click here for additional data file. 31 Aug 2022Factors Influencing the Practice of Smoking Cessation Assessment and Management among Primary Care Doctors in Three Districts of Malaysia (SCAAM-DOC)PONE-D-22-17659R1Dear Dr. Ling,We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at onepress@plos.org.If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact Kind regards,Billy Morara Tsima, MD MScAcademic EditorPLOS ONEAdditional Editor Comments :The reviewers' comments and suggestions are satisfactorily addressed and the manuscript has been improved.Reviewers' comments: 21 Sep 2022PONE-D-22-17659R1 Factors Influencing the Practice of Smoking Cessation Assessment and Management among Primary Care Doctors (SCAAM-DOC) in Three Districts of Malaysia Dear Dr. Ngee Ling:I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. onepress@plos.org.If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. 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Changes in RSA can be an adaptive strategy to avoid saline soils whilst optimising nutrient and water acquisition. In this review we propose a new model for designing crops with a salt-tolerant root ideotype. The proposed root ideotype would exhibit root plasticity to adapt to saline soils, root anatomical changes to conserve energy and restrict sodium (Na+) uptake, and transport mechanisms to reduce the amount of Na+ transported to leaves. In the future, combining high-resolution root phenotyping with advances in crop genetics will allow us to uncover root traits in complex crop species such as wheat, that can be incorporated into crop breeding programs for yield stability in saline soils.Crop yields must increase to meet the demands of a growing world population. Soil salinization is increasing due to the impacts of climate change, reducing the area of arable land for crop production. Plant root systems are plastic, and their architecture can be modulated to (1) acquire nutrients and water for growth, and (2) respond to hostile soil environments. Saline soils inhibit primary root growth and alter root system architecture (RSA) of crop plants. In this review, we explore how crop root systems respond and adapt to salinity, focusing predominately on the staple cereal crops wheat, maize, rice, and barley, that all play a major role in global food security. Cereal crops are classified as glycophytes  however salt-tolerance can differ both between species and within a species. In the past, due to the inherent difficulties associated with visualising and measuring root traits, crop breeding strategies have tended to focus on optimising shoot traits. High-resolution phenotyping techniques now make it possible to visualise and measure root traits in soil systems. A  Wheat, grown in temperate regions, is the largest food crop in the world, providing approximately 20% of human calories and protein  and post-embryonic  roots Figure 1Root hairs are specialised tubular structures that arise from epidermal cells. They are crucial for nutrient and water capture as they extend into the soil and increase the root surface area. The growth and development of root hairs are modulated by environmental signals, ensuring optimal acquisition of nutrients. The availability of less mobile nutrients such as inorganic P (Pi), iron (Fe), zinc (Zn) and magnesium (Mg) can all affect root hair morphogenesis . Root ha3Gossypium hirsutum L.) plants in saline solution by Salinity inhibits root system growth less than shoot growth, and the root:shoot biomass ratio increases in most species including sorghum , wheat , and ricArabidopsis 2 in the medium to prevent the Ca2+ activity falling in the presence of NaCl. The addition of NaCl or any electrolyte decreases the activity (the concentration of the free Ca2+ ion). For instance, addition of 150 mM NaCl to a solution containing 2 mM Ca(NO3)2  decreases the activity of free Ca2+ to 0.64 mM (2+ activity from 0.64 to 3.15 mM by increasing the Ca(NO3)2 from 2 to 10 mM in the presence of 150 mM NaCl resulted in a 50% increase in root system biomass in wheat , the meristematic zone extends to about 1 mm from the tip, and the elongating zone another 2 mm from the tip (3)2 was supplied at 4 mM which would be sufficient to maintain the Ca2+ activity above 1 mM), but at 250 mM NaCl cell division ceased (Sorghum bicolor L.)  .3.2+ and Cl- on root growth is considered secondary in time to the initial osmotic effect of salinity, which cause the same rapid decrease in root growth as do non-ionic osmotica such as mannitol and polyethylene glycol. That is, the initial effects are from the salt outside the roots, not on the inside  for roots to extract water, resulting in an ‘osmotic stress’ similar to that occurring with water deficit  effect of external concentrations of Nae inside . High co deficit .+ concentrations increased up to 17-fold compared to control conditions and the K+/Na+ ratio significantly decreased. However, root (and shoot) ion concentrations did not correlate with root elongation rates, showing that the Na+ and K+ concentrations in the mature zones of roots were not influencing root growth, at least during the early phase of salt stress (Solanum lycopersicum L.); the most salt-sensitive genotypes (with respect to root growth) had the highest root Na+ content and lowest K+/Na+ ratio  and lower in the inner cells and xylem vessels. In contrast, at 50 mm from the apex, Na+ and Cl- were high in the pericycle and lower in the outer tissues.Ion concentrations in plants subject to salinity stress have routinely been measured in whole plant tissues . To understand the direct impact of cellular Naat roots  and in bat roots . Differeoot apex . Closer + however as the dye is non-ratio metric measurements cannot by quantified . In most higher plants including crop species, Na+ appears to reach toxic concentrations before Cl- and therefore is thought to be more damaging to the plant (Citrus sp.) and grapevine (Vitis vinifera L.), Cl- concentrations in the leaf blades rise faster than Na+ (Na+ is retained in stems and petioles) and so Cl- is considered as the more toxic ion  is reduced which has potentially negative effects on cellular metabolism and signalling  and lowest in the inner cortical cells (22 mM)  and low in the xylem parenchyma (34 mM). The K+ profiles were generally inverse to those of Na+. Chloride was only detected in the epidermis. These data suggest that the epidermal and cortical cells removed most of the Na+ and Cl- from the transpiration stream before it reached the endodermis, and that the endodermis is not the control point for salt uptake by the plant. The pericycle as well as the xylem parenchyma may be important in the control of net Na+ loading of the xylem, their vacuoles acting as sites for storage.In durum wheat grown at 50 mM NaCl, X-ray microanalysis of mature roots showed that the Na (22 mM) . Na+ was+ and Cl- to avoid ion toxicity, while ensuring sufficient solutes for osmotic adjustment. Root cells accumulate sufficient solutes to match, in osmolarity, the increased ion concentrations in the soil solution. This ‘osmotic adjustment’ maintains cell turgor and the volume of organelles within the cells of the growing plant. Taking up just 2% of the NaCl allows a plant to osmotically adjust the Na+ and Cl– in vacuoles, while organic solutes provide the balancing osmotic pressure in the cytoplasm  and ionic  stress. However, limited progress has been made because of the common usage of ‘salt shock’, a transfer of plants growing in non-saline hydroponic solutions to very high concentrations of NaCl or mannitol. This immediately causes a very large but transient rise in the concentration of cytosolic Ca2+. In Arabidopsis, OSCA1 is a hyperosmolality-gated calcium-permeable channel that results in increased cytosolic Ca2+  sphingolipids, that is proposed as a potential salt sensor  Lam), resulted in a decrease in the accumulation of Na+ in root cells in response to salt stress  also led to increased shoot Na+ content under salt conditions, indicating that the SOS pathway is crucial for salt tolerance  pathway, first discovered in Arabidopsis is undoubtedly critical for salt tolerance . The SOStiporter Figure 3ey roots . In maizolerance .5Root system architecture (RSA) refers to the spatial arrangement of the whole root system in the soil medium, modulated by root angle, type and rate of growth . The roo5.1shovel-omics techniques . Non-destructive 2D high-throughput root phenotyping platforms include GrowScreen-Rhizo , Phytomochniques .The development of non-destructive 3D phenotyping technologies such as X-ray computed tomography (CT), magnetic resonance imaging (MRI) and positron emission tomography (PET) , root growth and branching patterns were modified in response to salinity with primary root growth inhibited and branch root elongation promoted (Triticum turgidum L. ssp durum), seminal root growth was inhibited more than branch roots in three out of four genotypes examined  in the roots  was identified to be a DRO1 homolog in rice  and appears to be involved in the root gravitropic response  observed . Whetherobserved . For rooobserved . Root besos1 and sos2 mutants showed halotropic responses at lower thresholds of NaCl . The molecular mechanism for halotropism is due to the redistribution of the hormone auxin through the regulation of the auxin protein PIN2 . Halotro of NaCl . Taken tresponse . WhetherSoil salinity, in both natural and managed environments (rain-fed and irrigated), is highly heterogeneous, and understanding how plants respond to this spatial and temporal heterogeneity is important for sustainable agriculture in more marginal lands. Split-root studies have shown how plants compensate for water and or nutrient deficiency, by optimizing root-foraging in the most favourable parts of the soil. This also applies to a saline soil. A review of halophytes grown with roots split between low and high salinity media, versus uniformly saline media, showed that water uptake from the least saline part of the soil or solution was the key factor driving shoot growth. Plants with part of the root at low salinity (0–10 mM NaCl) had 3- to 10-fold higher shoot dry mass than plants with roots in uniformly saline (50–800 mM NaCl) media .in situ if there is a history or ‘memory’ of frequent rain or irrigation that dilutes the salt that builds up around the roots or continue the exploration for low salt patches. The latter could become very expensive in terms of carbon cost.Plants exclude 97-98% of the salt, as remarked in the Introduction, so unless the soil receives frequent rainfall, or irrigation, the salt will build up to concentrations that prevent uptake further uptake of water. This implies that roots in saline soils without irrigation must continually forage for low-salt patches while balancing the need for nutrients. The root system architecture must be flexible enough to continue 7+  from rice  and Nax2  from the diploid wheat ancestor Triticum monococcum were introgressed into durum wheat Tamaroi and have both been shown to remove Na+ from the xylem in roots  and retrieves Na+ from the xylem  and have different root types and anatomical features compared to the dicot Arabidopsis. It is therefore likely they will have different mechanisms to adapt to saline soils. Understanding the root response to salt stress is complicated as it is dependent on species, genotype, plant age, and the intensity and duration of the stress. Many root studies have also been carried out in the laboratory in pots filled with soil or on media with artificial substrates, with very limited field studies. The advent of new high-throughput phenotyping platforms and technologies will help us to gain a better understanding of the diversity of root responses to saline soils across species. In the future, it will also be imperative to assess root traits in the field, and capture root responses to heterogenous soils.salt-tolerant root ideotype for designing a root system adapted to saline soils. The salt-tolerant root ideotype would include root plasticity to allow roots to avoid highly saline soils whilst maximising water and nutrient uptake, transport mechanisms to exclude Na+ from the root and root anatomical traits to restrict Na+ movement into the xylem and conserve energy (Exploiting root traits with specific ideotypes in crop breeding has been shown to be successful for nitrogen acquisition and water capture in maize . Salinite energy Figure 3MS and RM both contributed to writing the article. All authors contributed to the article and approved the submitted version.


ObjectivesPandemics, natural disasters, and other unforeseen circumstances can cause short-term variation in radiotherapy utilization. In this study, we aim to develop a model to forecast linear accelerator (LINAC) utilization during periods of varying workloads. Methods: Using computed tomography (CT)-simulation data and the rate of new LINAC appointment bookings in the preceding week as input parameters, a multiple linear regression model to forecast LINAC utilization over a 15-working day horizon was developed and tested on institutional data. Results: Future LINAC utilization was estimated in our training dataset with a forecasting error of 3.3%, 5.9%, and 7.2% on days 5, 10, and 15, respectively. The model identified significant variations (≥5% absolute differences) in LINAC utilization with an accuracy of 69%, 62%, and 60% on days 5, 10, and 15, respectively. The results were similar in the validation dataset with forecasting errors of 3.4%, 5.3%, and 6.2% and accuracy of 67%, 60%, and 58% on days 5, 10, and 15, respectively. These results compared favorably to moving average and exponential smoothing forecasting techniques. Conclusions: The developed linear regression model was able to accurately forecast future LINAC utilization based on LINAC booking rate and CT simulation data, and has been incorporated into our institutional dashboard for broad distribution. Advances in knowledge: Our proposed linear regression model is a practical and intuitive approach to forecasting short-term LINAC utilization, which can be used for resource planning and allocation during periods with varying LINAC workloads. However, as we describe below, the COVID-19 pandemic caused significant disruptions to our departmental operations, highlighting the need for a robust forecasting model to predict and guide near-term linear accelerator (LINAC) utilization (LU).In anticipation of a late pandemic scenario, our department assembled a multidisciplinary task force to ensure there was always an adequate workforce to facilitate RT delivery for all cancer patients. Staffing across all disciplines was tracked daily, and in parallel, LU was monitored as well. One of the challenges identified was that in addition to matching current RT demand to workforce supply, there was a need to estimate these variables up to 3 weeks later, as new patients seen in consultation currently and planned for RT may be impacted at that later time. We had no direct way to estimate the impact of change in new patient consultations, treatment deferrals, altered fractionation regimens, or variation in cancer presentation and treatment intents on LU. Additionally, in a large department such as ours with >8000 new cancer patients seen annually, it was critically important that all staff were aware of the current and future LU to guide critical decision making including whether to defer nonessential treatments if necessary.5 In terms of forecasting LU, previous studies have focused on modeling patient flow and using simulation models to derive the LU.68 In this study, we describe a simple model-based approach that can guide the ramp-up and ramp-down of RT delivery in periods of varying workloads.A review of the operations research literature was performed, prior to developing an LU forecasting tool. The majority of the published work has focused on optimizing patient scheduling processes for a given patient workload.iP (i = days 1-15), was calculated using equation (iHB) for the next 15 days, and the remaining LINAC capacity  in normal operation, iC (equation [iM. The relative correction factor iM depends on the average variation in the rate of LINAC appointment booking (S) in the last 5 days. Both average LINAC appointment booking rate variation (S) were normalized by their corresponding average value during normal operations.The study design was a retrospective simulation using LINAC and computed tomography (CT)-simulator booking data from our institution. The booking workflow for our department is that most patients receiving curative treatment are booked for CT simulation first. Once an acceptable RT plan has been created by the dosimetrist, the patient is booked for LINAC appointments. Conversely, most patients receiving palliative or urgent treatment are booked for CT simulation and LINAC appointments at the same time. The proposed method for LU forecasting utilized a multiple linear regression model to predict the daily LU over a 15-working day horizon. The predicted LU, equation ), modulaB) and CT-simulator utilization (S) was translated into LU in the model using the weighting factors bw and sw (equation [B) and the CT-simulator utilization (S) are not independent variables, and day-dependent weighting factors i,bt and i,st  were introduced to model the effect that LINAC appointment booking rate may preferentially impact LU in the first 10 days, whereas CT-simulator utilization would likely impact the LU later in the prediction horizon, given that there is a lag between CT-simulator and treatment start dates. Both i,bt and i,st were defined as a 1-dimensional vector (i = days 1-15) and their values were changed as part of the bw and sw optimization. The LINAC appointment booking rate variation (ΔB) was computed using a 5-day difference in booked LINAC hours on days 1 to 15. The average LINAC appointment booking rate variation (S) was calculated over 5 days, with the use of an exponential smoothing filter to reduce daily variation.The effect of variations on LINAC appointment booking rate (Δequation ). These iHB is the number of hours booked on LINAC on day i = 1 to 15 from now. iC is the remaining hours of LINAC capacity during normal operation. iM is an activity-based relative correction factor. i,HBnormal is the number of hours booked on LINAC on day i = 1 to 15 from now during normal operation. Fnormal is the total average LU during normal operation. i,bt is a 1-dimensional vector of constant weighting factors for the booking rate ratio for day i = 1 to 15. i,st is a 1-dimensional vector of constant weighting factors for the CT simulation ratio for day i = 1 to 15:i,bt + i,st = 1bw is the optimized weighting factor for the booking ratio on LINAC. sw is the optimized weighting factor for the CT simulation ratio. S is the number of CT simulation appointments in the past 5 days before the day of prediction. LU, LINAC appointment booking rate, and CT-simulator utilization data were extracted from our radiation oncology information system  from January 1 to September 23, 2020, to develop and test our predictive model. The data was processed to remove duplicate entries and associate each CT simulation appointment with a unique treatment course. The date range was selected to cover 3 months of normal operation prepandemic, and the subsequent 6 months of the pandemic during which significant variations in LU were observed. The performance of the model was assessed by comparing the actual and predicted LU over the entire data period. In addition to testing the numerical differences between the actual and predicted LU, the ability of the model to predict significant variations in LU (>5% absolute change) was investigated. If a >5% change was forecasted in the specified horizon, this was “flagged” as a significant increase or decrease in LU. Confusion matrices were generated to illustrate the recall, precision, and accuracy of the flagged changes. The model was tested in an independent validation cohort, on additional 6 months of data from January to June 2021. The performance of the multiple linear regression model was also compared with the 10-day moving average and simple exponential smoothing forecasting techniques:Supplemental Appendix Figure A2 details how the average number of fractions/treatment course and LINAC time per fraction varied. Overall there was a trend toward hypofractionation with the average number of fractions per new course being 14.4 for January to March 2020 versus 11.9 between April and June 2020 versus 11.6 between July and September 2020. The average LINAC time per fraction did not vary significantly during this time period ranging from 21 to 22 min per appointment. The average waiting time from CT simulation to the first LINAC fraction was 9.0 days. Our institution houses 16 LINACs and 4 CT simulators and data regarding machine utilization was retrieved from the radiation oncology information system. In this study, LU was tracked from January 1 to September 23, 2020. In total, 4448 unique courses of LINAC-based RT corresponding to 56,484 fractions were delivered during this period. In response to the institutional need to forecast future LU, we present a simple method to calculate this parameter over a 3-week horizon. Using booking rate and CT simulation data as input parameters a multiple linear regression model was developed. The performance of the model was evaluated and a forecasting error of 3.3%, 5.9%, and 7.2% on days 5, 10, and 15, respectively, were observed. In addition to providing an estimate of future LU, the other utility of this model is to flag any significant changes in order to guide resource planning. The threshold of 5% for a “significant” change was chosen to represent the workload of approximately 1 LINAC, which is staffed by 3 radiation therapists. Until day 15, the performance of the flagging system was reasonable with recall precision and accuracy above 60% for most instances.6 For example, recently Vieira et al7 used discrete-event simulation (DES) to investigate various combinations of push and pull strategies in RT scheduling. The DES model used several inputs including patient arrival rates, processing time, and resource availability.7 Similarly Babashov et al8 used similar inputs to model patient flow in a large RT department and determine the effect of reduced human resources or increased patient volumes on waiting time targets. Thomas9 developed a Monte-Carlo simulation to estimate the LU and determined the spare capacity required to keep patient waiting time low. While simulation models can determine LU by modeling patient flow and determining the number of hours booked on LINACs, the main objectives of the simulation models are to test different scheduling strategies and resource allocation, in order to improve target performance such as reduced patient waiting times. Our forecasting model focuses on using the current CT simulation and LINAC booking rate to estimate the short-term variation of the LU, so the department can plan its resources ahead of time when there is varying patient demand and workload. Further work is now being performed to improve the model performance and prediction horizon using additional input parameters such as radiation consultation information and using more advanced modeling methods. It will be interesting to compare the performance of the proposed linear regression model with more sophisticated modeling techniques such as DES, since the expertise and software for performing more advanced modeling techniques may not be available at all institutions.The advantage of this model is that it utilizes a generalizable framework for LU forecasting using intuitive parameters such as booking rate and CT simulations. Time-adjusted continuously updating weighted factors account for potential practice changes and variation such as the trend toward hypofractionation as observed in this dataset. There has been other work performed to estimate machine utilization by modeling patient flow in the RT department and healthcare settings.Suuplemental Appendix Figure A3) and the forecasting performance was similar to the results during the pandemic with a forecasting error of 3.4%, 5.3%, and 6.2% on days 5, 10, and 15, respectively. In this validation cohort, the performance of the linear regression model compared favorably to the moving average and exponential smoothing forecasting techniques. Specifically, the accuracy of the linear regression model was highest for days 5, 10, and 15 LU forecasts. This supports the inclusion of “predictive” elements with CT simulation data, which may improve forecasting performance during periods of fluctuating/changing workloads given that averaging and smoothing forecasting techniques are based on the assumption that the mean is slowly varying.One limitation of this work is the need for independent validation on another dataset before robust conclusions can be drawn. The primary challenge with validating this model using separate data is that historically there have been no other time periods in the last 10 years where LU has varied by more than 5% in consecutive weeks; therefore, there is an absence of real-world data to validate this approach. We did test the model on an additional 6 months of data in 2021 when our LU patterns were stabilized and closer to normal operations  and our ability to pool treatment delivery resources greatly reduce the impact of breakdowns on our treatment capacity. However, the forecasting model presented in this work was designed to guide operational strategies related to LU and staffing assignments in situations of both overcapacity and undercapacity. The main utility of this model was during the COVID-19 pandemic when the department was asked to reduce the LU to a target of 60% to 70% during the peak infectious period and ramp-up to full operations after resolution. Access to forecasted LU allowed for a coordinated departmental effort in patient prioritization (to reduce infectious exposure) as well as ensuring adequate infrastructure and staff assignment during the ramping down and up periods. Also, the methods described in this paper to forecast LU are agnostic to institutional capacity and could potentially be scaled to smaller institutions that may have different operational challenges. We recognize that operational characteristics and workflows at other institutions may differ and therefore this framework may need to be adapted for the individual needs of other institutions.10 presented a tool to optimize capacity in patients with different waiting times. The model assumes a relatively static case mix citing data that the patterns of practice did not significantly change over 1 year. Whereas Crop et al11 presented a DES-based Constant-Work-In-Progress model to account for long in-room times high variability and high rate of cancelations. Scheduling models are also used to optimize patient appointment assignments on different LINACs. For example, Saure et al12 formulated a Markov decision process model, and Conforti et al13 developed a mixed integer programming model to allocate treatment capacity to incoming demand. In these approaches, forecasting machine utilization is not required.This model only addresses the forecasting component of RT scheduling, and there is ongoing work to develop a schedule optimizer based on the forecasted workload. The approach to schedule optimization is dependent on how incoming patient flow is managed. For example, Thomsen and NørrevangIn summary, we have developed a linear regression model to forecast LU which has now been incorporated into clinical dashboards for prospective validation and departmental use. The results of this algorithm can guide human resource planning and LINAC staffing, maintenance scheduling, patient scheduling as well as programmatic planning during periods of varying workload.In this study, we present a practical method to forecast future LU based on CT simulation data and the rate of new bookings in the preceding week. This model or variations of this approach could be used for resource planning during periods of varying LINAC workload.Click here for additional data file.Supplemental material, sj-docx-1-tct-10.1177_15330338221123108 for Forecasting Institutional LINAC Utilization in Response to Varying Workload by Srinivas Raman, Fan Jia, Zhihui Liu, Julie Wenz, Michael Carter, Colleen Dickie, Fei-Fei Liu and Daniel Letourneau in Technology in Cancer Research & Treatment


Eichhornia crassipes) was monitored in the Lower Mondego region . For this purpose, Sentinel-2 satellite data were explored enabling us to follow spatial patterns in three water channels from 2018 to 2021. By applying a straightforward and effective methodology, it was possible to estimate areas that could contain water hyacinth and to obtain the total surface area occupied by this invasive species. The normalized difference vegetation index (NDVI) was used for this purpose. It was verified that the occupation of this invasive species over the study area exponentially increases from May to October. However, this increase was not verified in 2021, which could be a consequence of the adopted mitigation measures. To provide the results of this study, the methodology was applied through a semi-automatic geographic information system (GIS) application. This tool enables researchers and ecologists to apply the same approach in monitoring water hyacinth or any other invasive plant species in similar or different contexts. This methodology proved to be more effective than machine learning approaches when applied to multispectral data acquired with an unmanned aerial vehicle. In fact, a global accuracy greater than 97% was achieved using the NDVI-based approach, versus 93% when using the machine learning approach (above 93%).Monitoring invasive plant species is a crucial task to assess their presence in affected ecosystems. However, it is a laborious and complex task as it requires vast surface areas, with difficult access, to be surveyed. Remotely sensed data can be a great contribution to such operations, especially for clearly visible and predominant species. In the scope of this study, water hyacinth ( Eichhornia crassipes) is a free-floating macrophyte native to the Amazon basin and adapted to lentic habitats. Its high ornamental value made it spread globally since the late nineteenth century. Nowadays, water hyacinth is present in lakes, reservoirs, ponds, irrigation ditches, and the final sections of rivers on all continents except Antarctica. It is in the top 100 of the most exotic and considered one of the most aggressive and predominant invasive species in the world [Eichhornia crassipes reproduction is defined by asexual reproduction and also by the production of a high number of seeds —a single inflorescence with 20 flowers can produce about 3000 seeds, which can remain viable for about 20 years—providing a competitive advantage over other native macrophytes [Water hyacinth  present the most susceptibility to water hyacinth invasions . HoweverRemote sensing and specifically, satellite imagery data analysis, are added-value tools to investigate and monitor water hyacinth invasion dynamics on a large scale. Indeed, and despite the low spatial resolution of satellite images when compared to high spatial resolution images obtained by unmanned aerial vehicles (UAVs), the correlation between the two types of data is high, making satellite images, namely Sentinel-2 imagery, appropriate tools for spatiotemporal studies . Thus, iDecision making in the management and control of water-hyacinth-affected ecosystems needs constant local monitoring to assess the invasion extent . The effThamaga and Dube  stated tThe area analyzed in this study is in central Portugal, within the Mondego River basin, between the cities of Coimbra and Figueira da Foz, a region known as Lower Mondego (Baixo Mondego in Portuguese). It is mostly composed of rice fields and three channels were selected to be monitored. These locations have diversion channels with potential accumulation of water hyacinth . A totalTwo types of remote sensing data were used to carry out this study: Sentinel-2 Multispectral Instrument (MSI) imagery and UAV multispectral imagery. The former group of images constitutes the core of this study, while the latter were used for validation purposes. The spectral data products provided by the MSI range from the visible to the short-wave infrared parts of the electromagnetic spectrum. Thirteen spectral bands (B) are available, in total, with different spatial resolutions central wavelengths: (1) with 10 m—B2 (490 nm), B3 (560 nm), B4 (665 nm) e B8 (842 nm); (2) with 20 m—B5 (705 nm), B6 (740 nm), B7 (783 nm), B8a (865 nm), B11 (1610 nm) e B12 (2190 nm); and (3) with 60 m—B1 (443 nm), B9 (940 nm) e B10 (1375 nm). The acquired data were projected onto a 100 km × 100 km Universal Transverse Mercator (UTM) grid.The data were downloaded using the Mundi Web Services platform. The search query respected the following conditions: Level 2A data products, corresponding to Bottom-Of-Atmosphere (BOA) reflectance; containing a cloud cover percentage below 10%; and between May 2018 and October 2021. Thus, all 10 m spatial resolution bands were downloaded , as well as a true color image (TCI) representation of each period. UAV-based data were obtained on 21 July 2021 in the locations numbered in The methodology implemented consists of a logical sequence of steps in order to obtain the spatial distribution of water hyacinth. If applied in a temporal perspective, this methodology allows measurement of the temporal evolution of the area covered by water hyacinth and its spatial distribution. In this way, it will be possible to simplify the observation of seasonal dynamics in the study area and estimate which areas are most affected.From the remotely sensed data, vegetation indices were computed, enabling us to highlight vegetation by considering differences obtained from arithmetic operations towards the available spectral bands. Usually, vegetation indices present higher values for healthy vegetation in comparison to non-vegetation elements, such as soil, dry vegetation, human-made infrastructures, and water. Since water hyacinth is mainly present in aquatic environments it is important to circumscribe the area to allocate the specimens to areas that meet the characteristics. Therefore, it is necessary to provide the polygons with locations that possibly contain water hyacinth. In the case of this study, a set of polygons delimitating the water channels was used in the analysis.After identifying the areas that can potentially include water hyacinth, the next step was to discard all pixels outside the polygons intended to be analyzed by merging them with vegetation-index data. This way, pixels only within the polygons were kept. Then a threshold value representing vegetation within the water channels was defined for a binarization process where all pixels above it were classified as one and equal or below the threshold value, as zero. This binarization process lead to the creation of a new image where bright pixels represented water hyacinth, which also allowed us to calculate its overall occupation area for each water channel as well as another raster product with the vegetation-index values within the polygons. This way, with the inclusion of different periods, the multi-temporal analysis and the detection of annual and seasonal changes was possible.Sentinel-2 MSI datasets were used to calculate the NDVI, which is widely used to analyze vegetation in different contexts. It uses NIR and red bands and is computed as shown in (1). Sentinel-2 MSI B8 (NIR) and B4 (red) were used to calculate the index. The same index was generated for the orthorectified UAV-based data in the three surveyed areas.A vector shapefile was created with the water channels intended to be analyzed . These dTo automate most aspects of the methodology, a GIS open-source application was developed, named QIASdetection (QGIS Invasive Aquatic Species detection) . It is oThe application allows users to detect invasive species in aquatic ecosystems by analyzing the NDVI . The input layers can be accessed from the QGIS canvas or from a user-specified directory. The user can define the NDVI filtering with two options: (1) by using a threshold value; or (2) by defining an interval by specifying minimum and maximum values. The application generates two outputs: (1) a raster identifying the invasive species ; and (2) a shapefile composed by descriptive statistics, such as the estimated area. The output files are stored in a folder defined by the user.To evaluate the water-hyacinth-detection accuracy when applying the methodology used in this study, two validation procedures were conducted using the acquired UAV-based data. One by conducting a pixel-wise comparison of the water hyacinth detection with manually digitized binary images of the UAV surveyed areas. The other assessment passed through the comparison of the obtained results (using the UAV-based NDVI) with the results obtained when applying a pixel-wise machine learning classification based on an RF  (the dat2) was evaluated.UAV data were also correlated with Sentinel-2 data for classification purposes. Due to differences in their spatial resolution—approximately 0.07 m for data acquired by UAV and 10 m for data from Sentinel-2—it is expected that some pixels may contain different elements. Therefore, to evaluate possible differences that may exist in the satellite data, the NDVI generated by the UAV data were resampled to the same spatial resolution as the NDVI generated with the Sentinel-2 data on 28 July 2021. A total of 100 pixels were selected that entirety cover water hyacinth areas and the correlation coefficient , followed by area 2 and area 1 with 5940 m2 and 5885 m2, respectively. The estimated water hyacinth coverage area for the three areas is presented in 2 were estimated in excess when using the NDVI. On the other hand, the RF estimation resulted in a total underestimation area of 1312 m2.The validation conducted when considering all NDVI values above 0.75 in the three areas surveyed by the UAV  while the RF classifier provided a mean detection rate of 93.31% . To analyze water hyacinth population growth and/or decline in the study area, Sentinel-2 time-series data were used dates in . Water hThe contribution of each water channel to the temporal changes of water hyacinth cover were analyzed in the monitored period . However, if the use of Sentinel-2 MSI data are considered, due to the nature of its spatial resolution and the reduced width of the water bodies, this type of approach can be inefficient, causing incorrect segmentation. The methodology is implemented in the form of a QGIS application. Therefore, a completely free approach is provided for monitoring water hyacinth presence in any area of the globe using Sentinel-2 MSI data or by using high-spatial-resolution UAV-based data. Thus, entities responsible for water hyacinth monitoring and control can use the proposed methodology in a fast and intuitive manner, helping to support decision making in mitigating/controlling invasive aquatic species.As a future work, water hyacinth samples may be collected to correlate with the remotely sensed data to enable us to study the viability of directly estimating its biomass through vegetation indices from remotely sensed multispectral data. The continuous monitoring of the study area should continue to be performed to assess whether the results observed in 2021 are repeated or even decline, in order to assess the effectiveness of the applied mitigation measures.


SpTransformer (SpTrf) gene family encodes a set of proteins that function in the sea urchin immune system. The gene sequences have a series of internal repeats in a mosaic pattern that is characteristic of this family. This mosaic pattern necessitates the insertion of large gaps, which has made alignments of the deduced protein sequences computationally difficult such that only manual alignments have been reported previously. Because manual alignments are time consuming for evaluating newly available SpTrf sequences, computational approaches were evaluated for the sequences reported previously. Furthermore, because two different manual alignments of the SpTrf sequences are feasible because of the multiple internal repeats, it is not known whether additional alternative alignments can be identified using different approaches. The bioinformatic program, PRANK, was used because it was designed to align sequences with large gaps and indels. The results from PRANK show that the alignments of the internal repeats are similar to those done manually, suggesting multiple feasible alignments for some regions. GUIDANCE based analysis of the alignments identified regions that were excellent and other regions that failed to align. This suggests that computational approaches have limits for aligning the SpTrf sequences that include multiple repeats and that require inserted gaps. Furthermore, it is unlikely that alternative alignments for the full-length SpTrf sequences will be identified.The  R genes in higher plants , to alin genome . Althougn genome . We furtSpTrf gene sequences (n = 134) from the genomes of four sea urchins were compiled from the SpTrf genes identified in the sea urchin BAC library . The first exon and the intron were removed to focus the analysis on the second exon that encodes the mature protein , and franimal G . This daSpTrf gene sequences were translated to the deduced protein sequences in BioEdit [ver 7.2.5  as a by-product of the PRANK alignment process. Additional neighbor joining trees were produced from the PRANK alignments in GUIDANCE2 (http://guidance.tau.ac.il) using MEGAX.MEGAX [are.net/ ] was usehttp://wasabiapp.org/software/prank/  produced in webPRANK (NJGT-PRANK) (http://wasabiapp.org/software/prank/prank_differences/), and are due to low confidence in branch placement and short branch lengths. Errors in tree structure may lead to errors in the translated codon alignments. Therefore, a maximum likelihood tree, which is considered a more robust method for phylogeny construction of highly variable sequences  into an MSA rather than only point mutations . PRANK iequences –13, 22. equences , was proThe imperfect, tandem Type 1 repeats positioned at the N terminal end of the mature SpTrf proteins  are desiElements Er6 through Er10 as defined by the repeat-based alignment are positioned in the histidine rich region of the deduced proteins 3A. TheyA to G element patterns) because it has a highly variable number of histidines among the genes, and specific sequence variants of Er10 are associated with specific mosaics of elements  are a series of Type 2 to Type 6 repeats that make up a duplicated, interspersed element pattern of 6-2-4-2-3-5 . This reBecause S-Er10 was aligned with L-Er18 in the MLGT-PRANK alignment, the region including L-Er11 to L-Er16 located to the N terminal side of L-Er18 were aligned with a large gap in the S proteins S3. Thishttp://guidance.tau.ac.il/ver2) is used to evaluate the robustness of the PRANK alignments based on the reliability of columns , sequences, and individual amino acids for each sequence in the alignment. GUIDANCE scores are defined as the confidence level for specific positions in an MSA. The GUIDANCE2 server evaluates MSAs as a tertiary PRANK alignment that uses GUIDANCE2 (GUIDANCE2-PRANK) to report and visualize the GUIDANCE scores as bar graphs for each column. It was noted that the GUIDANCE2-PRANK alignment was different from the alignment generated by NJGT-PRANK. This was not unexpected given that PRANK has been documented as resulting in slightly different alignments from different runs of the same set of sequences (http://wasabiapp.org/software/prank/#Methods). Furthermore, while the GUIDANCE2 server only provided the GUIDANCE scores for the base or standard MSA, defined as the first alignment generated upon which additional phylogenetic trees and alignments were generated to calculate the GUIDANCE scores, additional MSAs were available as a SuperMSA file that contained alternative alignments generated by the program as a concatenation of the top 20 alternative PRANK aligned MSAs (data not shown). The base alignment from GUIDANCE2-PRANK showed the same type of alignment variations as those from the NJGT-PRANK alignment, which were also evident with most of the SuperMSA alignments to various degrees. For example, for the Type 1 repeats, the B2-10, E6-2, and C2-4 element pattern proteins with variable numbers of repeats resulted in three different gap insertions in all of the other protein sequences  that result in elements that are defined by gaps . TherefoTo determine whether there were other alternative alignments, GUIDANCE2 was also used to evaluate a ClustalW alignment  and the confidence scores were compared to those for the GUIDANCE2-PRANK alignment. The GUIDANCE2-ClustalW alignment had no regions of no confidence but was composed of long regions of low or variable confidence  to LCS and awards from the Wilber V. Harlan Trust through the Department of Biological Sciences and a Dissertation Fellowship from the Columbian College of Arts and Sciences at GWU to MABH.The authors are grateful to Helen Dooley for recommending the PRANK program for use with the SpTrf sequences. Leon Grayfer and Damien O’Halloran provided helpful critiques to improve the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.