Study Monitoring in Emergency Care Trials: Lessons from the Resuscitation Outcomes Consortium Continuous Chest Compressions Trial
Bibliographic record
Abstract
OBJECTIVE: Clinical trial investigators often assemble internal study monitoring committees (SMCs) to measure individual or group adherence with trial performance benchmarks. We examined the processes and results of study monitoring in an international trial of out-of-hospital cardiac arrest. METHODS: We studied SMC operations for the Resuscitation Outcomes Consortium (ROC) Continuous Chest Compressions (CCC) trial, which compared continuous with interrupted chest compressions upon survival after out-of-hospital cardiac arrest. The SMC defined trial performance benchmarks, which included compliance with the intervention, cardiopulmonary resuscitation (CPR) process data availability and timely data completion. Trial investigators received monthly performance reports. We determined rates of trial noncompliance and suspension from the trial. RESULTS: ROC-CCC enrolled a total of 23,711 subjects in the primary analysis population. Across 113 enrolling agencies, the SMC monitored performance for a total 2,367 agency-months. Emergency medical services agencies were on probation for a total of 178 (7.5%) agency-months. Fifty-five agencies were placed on probation at least once, of which 78% improved their performance and were approved for continued participation in the trial. A total of 12 agencies were suspended from trial participation. Data monitoring resulted in high-quality CPR (mean chest compression fraction = 0.80), 87% CPR process availability and timely data completion (75th and 95th percentiles prehospital data = 22 and 57 days; hospital data = 58 and 118 days). CONCLUSIONS: Study monitoring procedures may play an important role in ensuring the performance quality in acute care 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 | 0.634 | 0.788 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.012 | 0.025 |
| Insufficient payload (model declined to judge) | 0.003 | 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".