Reporting Quality of Noninferiority Randomized Controlled Trials for Skin Disease: A Meta-Epidemiological Study
Bibliographic record
Abstract
Noninferiority randomized controlled trials (NI-RCTs) aim to demonstrate that an intervention has acceptable efficacy compared with an established treatment. We assessed the reporting quality of NI-RCTs evaluating treatments for skin conditions. We searched MEDLINE and Embase from inception to April 2024 for NI-RCTs published in top journals measured by h5-index. Screening, full-text review, and data extraction were conducted independently in duplicate. We measured items reported from the Consolidated Standards of Reporting Trials extension for NI-RCTs published after 2006. We included 71 NI-RCTs reporting, on average, 78% of overall and 67% of NI-RCT-specific items. Forty-seven (51.6%) studies reported the noninferiority hypothesis. Sixty-five (91.5%) studies reported a noninferiority margin, with 26 (36.6%) providing justification; 48 (67.6%) calculated sample size using the margin. Twenty-two (31.0%) studies conducted both intention-to-treat and per-protocol analyses, whereas 26 (36.6%) used intention to treat, and 11 (15.5%) used per protocol alone. For 17 studies (24.0%), reviewers reached conclusions different from those of authors (7%) or could not assess appropriateness of authors' conclusions owing to insufficient reporting (17.0%). Reporting of NI-RCTs for skin conditions is inconsistent, with crucial information missing from many publications. Improved reporting is essential to incorporating their results into clinical practice.
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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 | 0.324 | 0.624 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.019 | 0.056 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".