Is predictability of randomisation a historic concern: A survey of practice of recruiters to randomised controlled trials

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Some randomisation methods, however, aim to achieve better balance between groups being compared at the expense of predictability. This study aims to investigate the extent to which recruiters into RCTs attempt to predict upcoming allocations, hence how concerned trialists should be about using more predictable randomisation methods. Methods We designed a short survey asking recruiters questions about the practice of predicting allocations before randomisation. The survey was circulated to R&I departments of hospitals in England involved in recruiting to randomised controlled trials. Two versions of the survey were employed, one explicitly referencing “subversion” of the randomisation, and one that did not use this word to investigate whether the framing of the research changed participants responses. A summary of counts and percentages of responses is presented. Results 122 participants responded to the survey. 30% of responders felt that they may be able to predict the next allocation in a sequence dependent on the information available, with 20% responding that they currently work on a trial where themselves or a colleague has attempted to guess the next allocation. 65 received the version that included reference to “subversion”, whilst 57 received the alternative version. There was not a notable difference in responses for the two versions. Conclusion This survey provides evidence that some recruiters do attempt to predict what the upcoming allocation will be. Whilst we do not know the motivations behind these predictions, they highlight the need for researchers to produce unpredictable allocation sequences. 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F1000Research 2026, 15 :491 ( https://doi.org/10.12688/f1000research.177973.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Research Article Is predictability of randomisation a historic concern: A survey of practice of recruiters to randomised controlled trials [version 1; peer review: awaiting peer review] Cydney L Bruce https://orcid.org/0000-0001-5448-4440 1 , Alan Montgomery https://orcid.org/0000-0003-0450-1606 1 , Antony Ndungu 1 , Reuben Ogollah 1 , Christopher Partlett 1 Cydney L Bruce https://orcid.org/0000-0001-5448-4440 1 , Alan Montgomery https://orcid.org/0000-0003-0450-1606 1 , [...] Antony Ndungu 1 , Reuben Ogollah 1 , Christopher Partlett 1 PUBLISHED 09 Apr 2026 Author details Author details 1 Nottingham Clinical Trials Unit, School of Medicine, University of Nottingham, Nottingham, UK Cydney L Bruce Roles: Conceptualization, Formal Analysis, Writing – Original Draft Preparation, Writing – Review & Editing Alan Montgomery Roles: Conceptualization, Supervision, Writing – Review & Editing Antony Ndungu Roles: Conceptualization, Writing – Review & Editing Reuben Ogollah Roles: Conceptualization, Supervision, Writing – Review & Editing Christopher Partlett Roles: Conceptualization, Supervision, Writing – Review & Editing OPEN PEER REVIEW REVIEWER STATUS AWAITING PEER REVIEW Abstract Background Randomisation in randomised controlled trials (RCTs) is intended to guard against selection bias by creating unpredictable sequences. Some randomisation methods, however, aim to achieve better balance between groups being compared at the expense of predictability. This study aims to investigate the extent to which recruiters into RCTs attempt to predict upcoming allocations, hence how concerned trialists should be about using more predictable randomisation methods. Methods We designed a short survey asking recruiters questions about the practice of predicting allocations before randomisation. The survey was circulated to R&I departments of hospitals in England involved in recruiting to randomised controlled trials. Two versions of the survey were employed, one explicitly referencing “subversion” of the randomisation, and one that did not use this word to investigate whether the framing of the research changed participants responses. A summary of counts and percentages of responses is presented. Results 122 participants responded to the survey. 30% of responders felt that they may be able to predict the next allocation in a sequence dependent on the information available, with 20% responding that they currently work on a trial where themselves or a colleague has attempted to guess the next allocation. 65 received the version that included reference to “subversion”, whilst 57 received the alternative version. There was not a notable difference in responses for the two versions. Conclusion This survey provides evidence that some recruiters do attempt to predict what the upcoming allocation will be. Whilst we do not know the motivations behind these predictions, they highlight the need for researchers to produce unpredictable allocation sequences. READ ALL READ LESS Keywords Randomisation, predictability, subversion, survey, RCTs Corresponding Author(s) Cydney L Bruce ( [email protected] ) Close Corresponding author: Cydney L Bruce Competing interests: No competing interests were disclosed. Grant information: This work is funded by the Nottingham clinical Trials Unit (NCTU). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Copyright: © 2026 Bruce CL et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Bruce CL, Montgomery A, Ndungu A et al. Is predictability of randomisation a historic concern: A survey of practice of recruiters to randomised controlled trials [version 1; peer review: awaiting peer review] . F1000Research 2026, 15 :491 ( https://doi.org/10.12688/f1000research.177973.1 ) First published: 09 Apr 2026, 15 :491 ( https://doi.org/10.12688/f1000research.177973.1 ) Latest published: 09 Apr 2026, 15 :491 ( https://doi.org/10.12688/f1000research.177973.1 ) Introduction Randomisation is considered the gold standard for allocating participants to interventions within clinical trials as it eliminates potential biases during recruitment, the most notable being selection bias. 1 Selection bias occurs when there are systematic differences between treatment groups stemming from the recruitment process. To avoid this bias, trialists are concerned with ensuring that recruiters are unaware of and unable to predict upcoming allocations (allocation concealment). 2 Simple randomisation is inherently unpredictable, minimising the risk of selection bias. However, randomisation is commonly adapted to achieve balance with respect to demographic or clinical characteristics, which increases the risk of allocation sequences becoming predictable to recruiters and open to subversion. 3 From the literature, there are instances where inadequate allocation concealment has led to bias within trials, 4 – 6 and research has shown the harmful effects of this bias on treatment effect estimates. 7 , 8 However, most of these case studies are dated, and research shows that some trialists involved in randomisation method selection were sceptical that this remains an issue in modern trials. 9 Two previous studies, 10 , 11 have investigated recruiters’ behaviour when recruiting to trials and found evidence of prediction, however these studies were small and have not quantified the presence or extent of prediction attempts. This shows that whilst clinical trialists may believe that sequence prediction is not common, the literature suggests there is evidence of subversion happening. The aim of this study is to describe allocation sequence prediction among recruiters to UK clinical trials and to identify possible methods of prediction by site staff that researchers should consider when evaluating method predictability. Methods Design and setting We designed a cross-sectional survey targeted at researchers who recruit participants to clinical trials by undertaking at least one of the following activities: 1) Participant identification, 2) Eligibility assessment, 3) Taking of consent, 4) Accessing the randomisation system. Research departments of hospitals in England were contacted and requested to circulate a survey about predicting allocation to their staff involved in recruiting trial participants. 16 Trusts agreed to circulate to their staff. A participant information sheet with details on the aims of the survey and use of the data was included with the link to the survey (Appendix 1- refer extended data) and written consent was obtained from participants within the survey (Appendix 2- refer extended data). Survey design The questionnaire was designed using findings from focus groups 9 and experience from the study team to ensure that the questions would target the information we wanted to obtain. Questions centred around whether participants ever attempted to guess the next allocation in a sequence. Those who responded yes were asked to consider what information they would consider when making this guess. The survey was designed in Microsoft Forms and a copy is available as (Appendix 2- refer extended data). We hypothesised that the wording used to describe the aims of the study might influence the respondents’ answers, as previous research has shown that recruiters may be aware of the harmful effects of subversion to a study. 10 Therefore we created two versions of the consent page: one explicitly stated that the aim of the study was to identify the potential for “subversion” in clinical trials; the other version referred to “prediction”. The wording used in these two study descriptions are available in Appendix 2 (refer extended data). The wording of the questions in the two versions were otherwise identical. Due to technical challenges in randomly allocating questionnaire version to participants, at the start of the survey they were asked to select either questionnaire 1 or questionnaire 2, unaware of which contained which specific wording. Sample size The primary aim of this survey is to estimate the proportion of recruiters to randomised trials who currently attempt to guess the next allocation in a sequence. Assuming a population proportion of 50%, a sample size of approximately 100–400 respondents yields an absolute margin of error for a proportion of 5–10 percentage points ( Table 1 ). We aimed to obtain 200 responses to provide reasonable precision with a sample size that was achievable within the time and resources available. Table 1. Sample sizes required to achieve an absolute margin of error for a proportion of 5–10 percentage points. Margin of error * Sample size 5 383 6 267 7 196 8 150 9 119 10 96 * defined as half-width of 95% confidence interval Analysis Data are summarised using frequency counts and percentages with no formal statistical comparisons. Summaries include responses to each of the questions split by questionnaire received. We were also interested in exploring whether an individual’s belief as to whether it is possible to predict upcoming allocations had any bearing on whether they have attempted to predict future allocations. This is also split by the type of questionnaire received to explore the possibility that researchers who believed that predicting allocations may be possible would be less likely to admit to attempting to make predictions when the word subversion is used in the questionnaire consent page. Ethical approval and consent The survey received ethical approval from the University of Nottingham (FMHS 173–0424) on 6 th August 2024 and was sent to R&I departments in England. Full details are contained in the declarations. Written consent was obtained from participants within the survey (Appendix 2- refer extended data). Results Recruitment to the survey opened on 19 th August 2024 and closed on 5 th December 2024. Of the R&I departments that circulated the survey, responses had ceased after 123 participants had responded. One responder declined consent, leaving 122 responders to be included in the analysis. Of those, 65 selected questionnaire one and received the questionnaire preamble which specifically referenced “subversion”, whilst the other 58 selected questionnaire two and received the alternative version. Table 2 provides a breakdown of characteristics of recruiters, split by the questionnaire version that they received. Around half of respondents were research nurses, over 85% reported undertaking each task involved in patient recruitment and allocation, over 60% had 5 or more years experience, and 84% reported having worked on more than 5 trials. There were no notable differences in characteristics between participants completing each version of the questionnaire. Table 2. Demographic information for the sample. Questionnaire version Subversion (n = 65) Predictability (n = 57) Total (n = 122) Job role Research Nurse 34 (52%) 28 (49%) 62 (51%) Clinical Researcher Practitioner 17 (26%) 18 (32%) 35 (29%) Principle Investigator 3 (5%) 1 (2%) 4 (3%) Research Fellow 0 (0%) 2 (4%) 2 (2%) Doctor 2 (3%) 0 (0%) 2 (2%) Other 9 (14%) 8 (14%) 17 (14%) Randomisation Process Participant Identification 59 (91%) 52 (91%) 111 (91%) Eligibility assessment 54 (83%) 51 (89%) 105 (86%) Taking of consent 54 (83%) 50 (88%) 104 (85%) Accessing the randomisation system 58 (89%) 46 (81%) 104 (85%) Years involved in trial recruitment < 5 24 (37%) 24 (41%) 48 (39%) 5–10 23 (35%) 16 (28%) 39 (32%) 10–15 12 (18%) 10 (17%) 22 (18%) 15–20 3 (5%) 2 (3%) 5 (4%) 20+ 3 (5%) 5 (9%) 8 (7%) Number of trials recruited to 1 4 (6%) 0 (0%) 4 (3%) 2 2 (3%) 4 (7%) 6 (5%) 3 3 (5%) 4 (7%) 7 (6%) 4 2 (3%) 0 (0%) 2 (2%) 5 + 54 (83%) 49 (84%) 103 (84%) Previously worked on a trial that failed to conceal allocations Yes 6 (9%) 9 (16%) 15 (12%) No 59 (91%) 48 (84%) 107 (88%) Table 3 shows the findings of the survey in relation to predicting subsequent allocations. Around 30% of the sample responded that, dependent on the information available, they felt they may be able to predict the next allocation in a sequence. Two respondents answered yes to this question. A higher proportion of those in the prediction group reported knowing a colleague who had attempted to guess the next allocation (53% vs 38%) however unexpectedly, figures were very similar for participants when asked if they had ever tried to guess the next allocation (40% vs 37%). Additionally, around 20% of respondents stated that they currently recruit to a trial where they or a colleague have attempted to guess the next allocation. Table 3. Predicting in trials. Questionnaire version Subversion (n = 65) Predictability (n = 57) Total (n = 122) Do you think you would be able to predict the next allocation in a randomisation sequence? Yes 0 (0%) 2 (4%) 2 (2%) Maybe 1 20 (31%) 16 (28%) 36 (30%) No 45 (69%) 39 (68%) 84 (69%) Have you ever known a colleague try to guess the next allocation when recruiting? Yes 25 (38%) 30 (53%) 55 (45%) No 40 (62%) 27 (47%) 67 (55%) Have you ever tried to guess the next allocation when recruiting? Yes 24 (37%) 23 (40%) 47 (39%) No 41 (63%) 34 (60%) 75 (61%) On how many trials? 1 7 (11%) 5 (9%) 12 (10%) 2 5 (8%) 9 (16%) 14 (11%) 3 5 (8%) 6 (11%) 11 (9%) 4 1 (2%) 1 (2%) 2 (2%) 5+ 6 (9%) 2 (4%) 8 (7%) Do you currently recruit to any trials where you or a colleague have attempted to guess the next allocation? Yes 13 (20%) 12 (21%) 25 (20%) No 52 (80%) 45 (79%) 97 (80%) On how many trials? 1 7 (11%) 6 (10%) 13 (11%) 2 2 (3%) 1 (2%) 3 (2%) 3 1 (2%) 1 (2%) 2 (2%) 4 0 (0%) 2 (3%) 2 (2%) 5 + 3 (5%) 2 (3%) 5 (4%) 1 Maybe – (dependent on available information) Table 4 contains an additional exploration of the relationship between answers to the questions “Have you ever tried to guess the next allocation?” and “Do you currently recruit to any trials where you or a colleague have attempted to guess the next allocation?” conditional on their answer to the question “Do you think you would be able to predict the next allocation in a randomisation sequence?” Table 4. Prediction responses given that the participants thought they may be able to predict the next allocation. Questionnaire version Total (n = 122) Subversion (n = 65) Predictability (n = 57) Do you think you would be able to predict the next allocation in a randomisation sequence? Yes/Maybe (n = 20) No (n = 45) Yes/Maybe (n = 18) No (n = 39) Yes/Maybe (n = 38) No (n = 84) Have you ever tried to guess the next allocation when recruiting? Yes 11 (55%) 13 (29%) 10 (56%) 13 (33%) 21 (55%) 26 (31%) No 9 (45%) 32 (71%) 8 (44%) 26 (67%) 17 (45%) 58 (69%) Do you currently recruit to any trials where you or a colleague have attempted to guess the next allocation? Yes 8 (40%) 5 (11%) 6 (33%) 6 (15%) 14 (37%) 11 (13%) No 12 (60%) 40 (89%) 12 (67%) 33 (85%) 24 (63%) 73 (87%) There is no notable effect of the questionnaire version provided. Overall, respondents who thought they may be able to predict upcoming allocations reported higher proportions of ever (55% vs 31%) and currently (37% vs 13%), attempting to predict allocations. This suggests that while overall the rate of prediction seen currently in trials by participants or colleagues appears to be around 20%, this proportion is higher in those who believe that they may be able to correctly predict upcoming allocations. Responders that stated they had ever attempted to guess the next allocation were asked what information they would use to make this guess. Responses were reviewed and the key themes identified are listed in Table 5 . Forty-two responders stated that the previous allocation would factor into this choice. Other responses included using the characteristics of the participant, that the prediction was entirely a guess (based on no information) and a very small minority stated that elements of the study design (primarily potential side effects) could be used to identify previous allocations. Six responders specifically stated their prediction would be under the assumption that the treatments alternate. Seven respondents were clear in their statement that their prediction would be entirely a guess. Table 5. Summary of details given on information used to guess the next allocation. Questionnaire version Subversion (n = 65) Predictability (n = 57) Total (n = 122) If you have ever tried to guess the next allocation, what information would you use to make this guess Previous allocation 22 (34%) 20 (34%) 42 (34%) Patient characteristics 2 (3%) 4 (7%) 6 (5%) Total guess 4 (6%) 3 (5%) 7 (6%) Study design feature 1 (2%) 2 (3%) 3 (2%) Knowledge of the sequence 2 (3%) 0 (0%) 2 (2%) Discussion Overall, while research shows that recruiters are aware of the need for allocation concealment and blinding, our survey has shown that this does not stop recruiters from attempting to predict the next allocation, with 20% of respondents stating that predictions are made either by themselves or a colleague in ongoing recruiting trials. This proportion is similar to that found previously in a smaller study. 10 There is a suggestion that those who believe that it is possible to predict upcoming allocations could be more likely to attempt to do so, emphasising the importance of researchers designing randomised trials to guard against prediction as much as possible. There is limited research looking at the effect of predictability on the final trial results, due to the difficulty in both identifying when subversion has happened and in measuring the effect this has had on outcomes. 12 , 13 A methodological review, 14 identified a trend between allocation concealment adequacy and the effect size, though this was not statistically significant. Researchers have identified discrepancies in baseline characteristics that suggest subversion could be present. 4 However, if there is not an obvious flaw in the allocation concealment, and if subversion does not affect baseline characteristics sufficiently to be detectable, subversion would likely go unnoticed. A strength of this study is that the final sample size, which whilst smaller than the original target, is large compared to other surveys investigating prediction in RCTs, 10 , 11 which had sample sizes of 25 and 30 respectively. This allowed us to estimate the proportion of recruiters who attempted to guess the next allocation with a margin of error of 9. This was based on a conservative assumption that 50% of the respondents would report attempting to predict the next allocation, but the observed proportion is lower meaning the precision around the obtained proportions might not be markedly reduced despite the smaller sample size. Another strength is that the anonymous nature was likely to make responders feel safer to answer honestly, given previous research has shown that recruiters are aware that they should not be predicting allocations. 10 , 15 The respondents’ anonymity is also a limitation of this study as it means we cannot validate how representative the sample of survey respondents are compared to the population of recruiters to RCTs. The estimates given could be under-estimating the proportion of recruiters who attempted to guess allocations if those who attempted to guess allocations were less likely to respond to the survey. Equally, the estimate given could be over-estimating this proportion if those who attempt to guess allocations were more likely to respond to the survey. Another limitation is the self-randomisation of participants. Participants were asked to select questionnaire 1 or questionnaire two, with the assumption that there was unlikely to be a bias in this choice, however there is the potential this could have introduced selection bias. However, baseline characteristics of participants were collected to monitor the potential of this bias, and very little difference in groups was detected. Whilst the aim of these recruiters is likely not to subvert randomisation sequences, this research highlights: 1) that researchers will attempt to make predictions about future allocations, although we do not know the motivation behind these guesses 2) Some recruiters are aware that patterns can arise in sequences which emphasises the continued need for researchers to guard sequences from becoming too predictable. Declarations The survey received ethical approval from the University of Nottingham (FMHS 173–0424) on 6 th August 2024, and R&I departments across England were contacted to circulate the survey on 19 th August 2024. It was agreed that the survey would close after reaching the target sample size, or after 3 months (6 th November 2024), however the survey remained open for another month due to delays in recruiting in the first month. Availability of data Data is not made openly available however can be obtained subject to the Nottingham Clinical Trials Units Data Sharing Policy. To obtain this data, the steps are as follows: • 1. Contact [email protected] , to obtain the “Data Sharing and use request form” • 2. Once this form is completed and returned, the Data sharing committee will meet within 3 months to review the application. • 3. A decision will be reached and communicated to the applicant within 7 days. • 4. If successful, a deidentified version of the data used for analysis will be available. Extended data Additional figures (Appendix 1 & 2) are available at Nottingham Research data Management Repository. Repository name: (Supplementary data) - Is predictability of randomisation a historic concern: A survey of practice of recruiters to randomised controlled trials. DOI: http://doi.org/10.17639/nott.7576 https://rdmc.nottingham.ac.uk/handle/internal/12012 . 16 This project contains the following extended data: Appendix 1. Participant Information Sheet Appendix 2. Survey Data is available under the terms of the Creative Commons Attribution 4.0 International license (CC BY 4.0) . Acknowledgements We would like to thank all researchers who responded to the survey, as well as the Trials Methodology Research Partnership for their contribution towards this work. References 1. Kahan BC, Rehal S, Cro S: Risk of selection bias in randomised trials. Trials. 2015; 16 : 405. PubMed Abstract | Publisher Full Text | Free Full Text 2. Altman DG: Randomisation. BMJ. 1991; 302 : 1481–1482. PubMed Abstract | Publisher Full Text | Free Full Text 3. Berger VW: Randomization Technique, Allocation Concealment, Masking, And Susceptibility Of Trials To Selection Bias. J. Mod. Appl. Stat. Methods. 2003; 2 (1): 80–86. Publisher Full Text 4. Kennedy ADM, Torgerson DJ, Campbell MK, et al. : Subversion of allocation concealment in a randomised controlled trial: a historical case study. Trials. 2017; 18 : 204. PubMed Abstract | Publisher Full Text | Free Full Text 5. Yaffe MJ, Seely JM, Gordon PB, et al. : The randomized trial of mammography screening that was not—A cautionary tale. J. Med. Screen. 2022; 29 (1): 7–11. PubMed Abstract | Publisher Full Text | Free Full Text 6. Turner J, Russell D, Russell I, et al. : Baseline imbalance in a randomised trial: a cautionary tale. Care of the Critically Ill. 2006; 22 : 42–44. 7. Wood L, Egger M, Gluud LL, et al. : Empirical evidence of bias in treatment effect estimates in controlled trials with different interventions and outcomes: meta-epidemiological study. BMJ. 2008; 336 (7644): 601–605. PubMed Abstract | Publisher Full Text | Free Full Text 8. Wang Y, Parpia S, Couban R, et al. : Compelling evidence from meta-epidemiological studies demonstrates overestimation of effects in randomized trials that fail to optimize randomization and blind patients and outcome assessors. J. Clin. Epidemiol. 2024; 165 : 111211. PubMed Abstract | Publisher Full Text 9. Bruce CL, Iflaifel M, Montgomery A, et al. : Choosing and evaluating randomisation methods in clinical trials: a qualitative study. Trials. 2024; 25 . 10. Brown S, Thorpe H, Hawkins K, et al. : Minimization - reducing predictability for multi-centre trials whilst retaining balance within centre. Stat. Med. 2005; 24 : 215–221. Publisher Full Text 11. Hewitt CE, Torgerson DJ, Berger VW: Potential for technical errors and subverted allocation can be reduced if certain guidelines are followed: Examples from a web-based survey. J. Clin. Epidemiol. 2009; 62 (3): 261–269. PubMed Abstract | Publisher Full Text 12. Berger VW, Exner DV: Detecting Selection Bias in Randomized Clinical Trials. Control. Clin. Trials. 1999; 20 (4): 319–327. Publisher Full Text 13. Fives A, Russell DW, Kearns N, et al. : The Role of Random Allocation in Randomized Controlled Trials: Distinguishing Selection Bias from Baseline Imbalance. J Multidiscip Eval. 2013; 9 : 33–42. Publisher Full Text 14. Clark L, Schmidt U, Tharmanathan P, et al. : Allocation concealment: a methodological review. J. Eval. Clin. Pract. 2013; 19 : 708–712. Publisher Full Text 15. Schulz KF: Subverting Randomization in Controlled Trials. JAMA. 1995; 274 : 1456–1458. Publisher Full Text 16. Bruce CL, Montgomery A, Ndungu A, et al. : Supplementary data - Is predictability of randomisation a historic concern: A survey of practice of recruiters to randomised controlled trials. Nottingham Research Data Management Repository. 2025. Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 09 Apr 2026 ADD YOUR COMMENT Comment Author details Author details 1 Nottingham Clinical Trials Unit, School of Medicine, University of Nottingham, Nottingham, UK Cydney L Bruce Roles: Conceptualization, Formal Analysis, Writing – Original Draft Preparation, Writing – Review & Editing Alan Montgomery Roles: Conceptualization, Supervision, Writing – Review & Editing Antony Ndungu Roles: Conceptualization, Writing – Review & Editing Reuben Ogollah Roles: Conceptualization, Supervision, Writing – Review & Editing Christopher Partlett Roles: Conceptualization, Supervision, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information This work is funded by the Nottingham clinical Trials Unit (NCTU). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Article Versions (1) version 1 Published: 09 Apr 2026, 15:491 https://doi.org/10.12688/f1000research.177973.1 Copyright © 2026 Bruce CL et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Bruce CL, Montgomery A, Ndungu A et al. 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