Quality of Reporting of Neonatal and Infant Trials in High-Impact Journals
Bibliographic record
Abstract
OBJECTIVES: To perform a systematic review of the quality of reporting for randomized controlled trials (RCTs) with infants and neonates that were published in high-impact journals and to identify RCT characteristics associated with quality of reporting. METHODS: RCTs that enrolled infants younger than 12 months and were published in 2005-2009 in 6 pediatric or general medical journals were reviewed. Eligible RCTs were evaluated for the presence of 11 quality criteria selected from the Consolidated Standards of Reporting Trials guidelines. The relationships between quality of reporting and key study characteristics were tested with nonparametric statistics. RESULTS: Two reviewers had very good agreement regarding the eligibility of studies (κ = 0.85) and the presence of quality criteria (κ = 0.82). Among 179 eligible RCTs, reporting of the individual quality criteria varied widely. Only 50% included a flow diagram, but 99% reported the number of study participants. Higher quality of reporting was associated with greater numbers of study participants, publication in a general medical journal, and greater numbers of centers (P < .0001 for each comparison). Geographic region and positive study outcomes were not associated with reporting quality. CONCLUSIONS: The quality of reporting of infant and neonatal RCTs is inconsistent, particularly in pediatric journals. Therefore, readers cannot assess accurately the validity of many RCT results. Strict adherence to the Consolidated Standards of Reporting Trials guidelines should lead to improved reporting.
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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 | 0.552 | 0.845 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.013 | 0.013 |
| Bibliometrics | 0.038 | 0.038 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.005 | 0.003 |
| 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".