Reporting of data monitoring committees and adverse events in paediatric trials: a descriptive analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVES: For 300 paediatric trials, we evaluated the reporting of: a data monitoring committee (DMC); interim analyses, stopping rules and early stopping; and adverse events and harm-related endpoints. METHODS: For this cross-sectional evaluation, we randomly selected 300 paediatric trials published in 2012 from the Cochrane Central Register of Controlled Trials. We collected data on the reporting of a DMC; interim analyses, stopping rules and early stopping; and adverse events and harm-related endpoints. We reported the findings descriptively and stratified by trial characteristics. RESULTS: Eighty-five (28%) of the trials investigated drugs, and 18% (n=55/300) reported a DMC. The reporting of a DMC was more common among multicentre than single centre trials (n=41/132, 31% vs n=14/139, 10%, p<0.001) and industry-sponsored trials compared with those sponsored by other sources (n=16/50, 32% vs n=39/250, 16%, p=0.009). Trials that reported a DMC enrolled more participants than those that did not (median [range]): 224 (10-60480) vs 91 (10-9528) (p<0.001). Only 25% of these trials reported interim analyses, and 42% reported stopping rules. Less than half (n=143/300, 48%) of trials reported on adverse events, and 72% (n=215/300) reported on harm-related endpoints. Trials that reported a DMC compared with those that did not were more likely to report adverse events (n=43/55, 78% vs 100/245, 41%, p<0.001) and harm-related endpoints (n=52/55, 95% vs. 163/245, 67%, p<0.001). Only 32% of drug trials reported a DMC; 18% and 19% did not report on adverse events or harm-related endpoints, respectively. CONCLUSIONS: The reporting of a DMC was infrequent, even among drug trials. Few trials reported stopping rules or interim analyses. Reporting of adverse events and harm-related endpoints was suboptimal.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.077 | 0.192 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it