The impact of COVID-19 restrictions on participant enrollment in the PREPARE trial
Bibliographic record
Abstract
Background: At the initiation of the COVID-19 pandemic, restrictions forced researchers to decide whether to continue their ongoing clinical trials. The PREPARE (Pragmatic Randomized Trial Evaluating Pre-Operative Alcohol Skin Solutions in Fractured Extremities) trial is a pragmatic cluster-randomized crossover trial in patients with open and closed fractures. PREPARE was enrolling over 200 participants per month at the initiation of the pandemic. We aim to describe how the COVID-19 research restrictions affected participant enrollment. Methods: The PREPARE protocol permitted telephone consent, however, sites were obtaining consent in-person. To continue enrollment after the initiation of the restrictions participating sites obtained ethics approval for telephone consent scripts and the waiver of a signature on the consent form. We recorded the number of sites that switched to telephone consent, paused enrollment, and the length of the pause. We used t-tests to compare the differences in monthly enrollment between July 2019 and November 2020. Results: All 19 sites quickly implement telephone consent. Fourteen out of nineteen (73.6%) sites paused enrollment due to COVID-19 restrictions. The median length of enrollment pause was 46.5 days (range, 7-121 days; interquartile range, 61 days). The months immediately following the implementation of restrictions had significantly lower enrollment. Conclusion: A pragmatic design allowed sites to quickly adapt their procedures for obtaining informed consent via telephone and allowed for minimal interruptions to enrollment during the pandemic.
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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 | 0.201 | 0.356 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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; 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".