Reporting of Harms in Randomized Controlled Trials Published in Urology Journals: An Updated Analysis
Bibliographic record
Abstract
PURPOSE: Harms are often overlooked, but important, outcomes of randomized controlled trial reporting. Our goal was to determine if harms reporting has improved in high-impact urology journals. MATERIALS AND METHODS: in 2012 and 2020 were analyzed. Each randomized controlled trial was evaluated by 2 authors in a masked-duplicate fashion to evaluate for adherence to harms reporting guidelines recommended by the Consolidated Standards of Reporting Trials (CONSORT) group. RESULTS: = .01). Methods criteria demonstrating the greatest improvements included item #3 "which harms were assessed," item #4a "when harm information was collected," and item #4b "methods to attribute harm to intervention." Results sections with the most improvement in reporting include item #6 "reasons for patient withdrawal," item #8a "effect size for harms," and item #8b "stratified serious + minor harms." CONCLUSIONS: Reporting of adverse events in randomized trials published in several top urology journals has demonstrated marked improvement. Studies published in 2020 reported approximately 70% of CONSORT-Harms criteria-an increase of nearly 40% since 2004. While these improvements mark significant change, deficits remain present and should be addressed to provide clinicians with the most complete perspective possible.
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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 | 0.521 | 0.797 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.015 |
| Bibliometrics | 0.037 | 0.042 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".