Methods of Recruitment for Surgical and Perioperative Randomized Controlled Trials: A Rapid Review
Bibliographic record
Abstract
Due to the complex nature of surgical randomized controlled trials (RCTs), reaching target recruitment can be challenging. The primary objective was to report on characteristics of successful pilot surgical and perioperative RCTs and the methodological strategies implemented to optimize recruitment. The secondary objective was to provide recommendations for successful recruitment strategies for future surgical RCTs. Ovid MEDLINE, Ovid EMBASE, and Web of Science (via Ovid) databases were searched from 2012 to 2022. This review included surgical and perioperative pilot studies that met their recruitment targets. Study and recruitment characteristics were summarized, and potential relationships between study design and recruitment rate were assessed. Optimized recruitment strategies were extracted when reported. Of 4156 total articles identified, 255 underwent full-text screening, and 52 articles were included. Of the included pilot studies, 21% (n = 11) did not indicate a target sample size or recruitment rate. Recruitment methods were minimally reported in pilot studies for perioperative or surgical RCTs. Strategies to optimize recruitment included internal iterative evaluations of the recorded recruitment appointments and staged introduction of the study. Recruitment rate was not associated with invasiveness of intervention or burden of participation. Patient involvement is absent from current reports on methodological design and offers valuable opportunity to optimize recruitment. Recruitment strategies in perioperative and surgical RCTs can be optimized with iterative qualitative evaluation of the recruitment methods with input from the interdisciplinary research team.
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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 | 0.415 | 0.637 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.018 | 0.014 |
| Bibliometrics | 0.034 | 0.029 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 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".