Recruitment and retention of participants in UK surgical trials: survey of key issues reported by trial staff
Bibliographic record
Abstract
BACKGROUND: Recruitment and retention of participants in surgical trials is challenging. Knowledge of the most common and problematic issues will aid future trial design. This study aimed to identify trial staff perspectives on the main issues affecting participant recruitment and retention in UK surgical trials. METHODS: An online survey of UK surgical trial staff was performed. Respondents were asked whether or not they had experienced a range of recruitment and retention issues, and, if yes, how relatively problematic these were (no, mild, moderate or serious problem). RESULTS: The survey was completed by 155 respondents including 60 trial managers, 53 research nurses, 20 trial methodologists and 19 chief investigators. The three most common recruitment issues were: patients preferring one treatment over another (81·5 per cent of respondents); clinicians' time constraints (78·1 per cent); and clinicians preferring one treatment over another (76·8 per cent). Seven recruitment issues were rated moderate or serious problems by a majority of respondents, the most problematic being a lack of eligible patients (60·3 per cent). The three most common retention issues were: participants forgetting to return questionnaires (81·4 per cent); participants found to be ineligible for the trial (74·3 per cent); and long follow-up period (70·7 per cent). The most problematic retention issues, rated moderate or serious by the majority of respondents, were participants forgetting to return questionnaires (56·4 per cent) and insufficient research nurse time/funding (53·6 per cent). CONCLUSION: The survey identified a variety of common recruitment and retention issues, several of which were rated moderate or serious problems by the majority of participating UK surgical trial staff. Mitigation of these problems may help boost recruitment and retention in surgical trials.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.079 | 0.230 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".