Methods to improve patient recruitment and retention in stroke trials
Bibliographic record
Abstract
BACKGROUND: The success of randomized-controlled stroke trials is dependent on the recruitment and retention of a sufficient number of patients, but fewer than half of all trials meet their target number of patients. METHODS: We performed a search and review of the literature, and conducted a survey and workshop among 56 European stroke trialists, to identify barriers, suggest methods to improve recruitment and retention, and make a priority list of interventions that merit further evaluation. RESULTS: The survey and workshop identified a number of barriers to patient recruitment and retention, from patients' incapacity to consent, to handicaps that prevent patients from participation in trial-specific follow-up. Methods to improve recruitment and retention may include simple interventions with individual participants, funding of research networks, and reimbursement of new treatments by health services only when delivered within clinical trials. The literature review revealed that few methods have been formally evaluated. The top five priorities for evaluation identified in the workshop were as follows: short and illustrated patient information leaflets, nonwritten consent, reimbursement for new interventions only within a study, and monetary incentives to institutions taking part in research (for recruitment); and involvement of patient groups, remote and central follow-up, use of mobile devices, and reminders to patients about their consent to participate (for retention). CONCLUSIONS: Many interventions have been used with the aim of improving recruitment and retention of patients in stroke studies, but only a minority has been evaluated. We have identified methods that could be tested, and propose that such evaluations may be nested within on-going clinical 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 | 0.743 | 0.822 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.009 | 0.013 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".