ABSTRACT 103
Bibliographic record
Abstract
Background and aims: Randomized controlled trials (RCTs) are ideally adequately powered to detect an important difference in the primary outcome. Aims: To describe the methods and reporting of sample size estimates of RCTs of children in pediatric critical care. Methods: We included published English-language trials from the Evidence in Pediatric Intensive Care database (epicc.mcmaster.ca) of RCTs administering any intervention to children. We excluded trials conducted in pre-term infants, and cross-over trials. Results: 101 (40%) of 251 RCTs published between 1986 and 2013 reported the intended sample size and some detail of their sample size estimation. 18 (18%) reported or referenced the formula used for sample size calculations and 7 (7%) reported the software used. 42 (42%) cited published data upon which their assumptions were based. The effect size actually observed was smaller than that expected in the sample size calculation in 30 (73%) of the 41 RCTs with binary primary outcomes that reported the expected effect size. The median (IQR) expected relative risk was 2.00 (1.67, 2.67) and the observed relative risk was 1.32 (0.97, 1.74) p=0.002. 42 (17%) of trials reported conducting interim analyses, 76% of which were planned a priori. Of the 25 RCTs (25% of the trials reporting a sample size calculation) that were stopped early, 8 (32%) described a stopping rule. Conclusions: Reporting of sample size estimation in pediatric critical care RCTs remains sub-optimal. Researchers frequently over-estimate the expected treatment effect when planning RCTs. Reporting transparency needs to be improved.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.497 | 0.336 |
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".