The Conduct and Reporting of Child Health Research: An Analysis of Randomized Controlled Trials Published in 2012 and Evaluation of Change over 5 Years
Bibliographic record
Abstract
Objectives For child health randomized controlled trials (RCTs) published in 2012, we aimed to describe design and reporting characteristics and evaluate changes since 2007; assess the association between trial design and registration and risk of bias (RoB); and assess the association between RoB and effect size. Study design For 300 RCTs, we extracted design and reporting characteristics and assessed RoB. We assessed 5-year changes in design and reporting (based on 300 RCTs we had previously analyzed) using the Fisher exact test. We tested for associations between design and reporting characteristics and overall RoB and registration using the Fisher exact, Cochran-Armitage, Kruskal-Wallis, and Jonckheere-Terpstra tests. We pooled effect sizes and tested for differences by RoB using the χ 2 test for subgroups in meta-analysis. Results The 2012 and 2007 RCTs differed with respect to many design and reporting characteristics. From 2007 to 2012, RoB did not change for random sequence generation and improved for allocation concealment ( P < .001). Fewer 2012 RCTs were rated high overall RoB and more were rated unclear ( P = .03). Only 7.3% of 2012 RCTs were rated low overall RoB. Trial registration doubled from 2007 to 2012 (23% to 46%) ( P < .001) and was associated with lower RoB ( P = .009). Effect size did not differ by RoB ( P = .43) Conclusions Random sequence generation and allocation concealment were not often reported, and selective reporting was prevalent. Measures to increase trialists' awareness and application of existing reporting guidance, and the prospective registration of RCTs is needed to improve the trustworthiness of findings from this field.
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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 | 0.270 | 0.576 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.023 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".