Heterogeneity and Gaps in Reporting Primary Outcomes From Neonatal Trials
Bibliographic record
Abstract
OBJECTIVES: Clear outcome reporting in clinical trials facilitates accurate interpretation and application of findings and improves evidence-informed decision-making. Standardized core outcomes for reporting neonatal trials have been developed, but little is known about how primary outcomes are reported in neonatal trials. Our aim was to identify strengths and weaknesses of primary outcome reporting in recent neonatal trials. METHODS: Neonatal trials including ≥100 participants/arm published between 2015 and 2020 with at least 1 primary outcome from a neonatal core outcome set were eligible. Raters recruited from Cochrane Neonatal were trained to evaluate the trials' primary outcome reporting completeness using relevant items from Consolidated Standards of Reporting Trials 2010 and Consolidated Standards of Reporting Trials-Outcomes 2022 pertaining to the reporting of the definition, selection, measurement, analysis, and interpretation of primary trial outcomes. All trial reports were assessed by 3 raters. Assessments and discrepancies between raters were analyzed. RESULTS: Outcome-reporting evaluations were completed for 36 included neonatal trials by 39 raters. Levels of outcome reporting completeness were highly variable. All trials fully reported the primary outcome measurement domain, statistical methods used to compare treatment groups, and participant flow. Yet, only 28% of trials fully reported on minimal important difference, 24% on outcome data missingness, 66% on blinding of the outcome assessor, and 42% on handling of outcome multiplicity. CONCLUSIONS: Primary outcome reporting in neonatal trials often lacks key information needed for interpretability of results, knowledge synthesis, and evidence-informed decision-making in neonatology. Use of existing outcome-reporting guidelines by trialists, journals, and peer reviewers will enhance transparent reporting of neonatal 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 | 0.786 | 0.921 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.020 | 0.018 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.010 | 0.012 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".