Reporting of harms data in RCTs: a systematic review of empirical assessments against the CONSORT harms extension
Bibliographic record
Abstract
OBJECTIVE: To determine the standard of reporting of harms-related data, in randomised controlled trials (RCTs) according to the Consolidated Standards of Reporting Trials (CONSORT) statement extension for harms. DESIGN: Systematic review. DATA SOURCES: The Cochrane library, Ovid MEDLINE, Scopus and ISI Web of Knowledge were searched for relevant literature. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: We included publications of studies that used the CONSORT harms extension to assess the reporting of harms in RCTs. RESULTS: We identified 7 studies which included between 10 and 205 RCTs. The clinical areas of the 7 studies were: hypertension (1), urology (1), epilepsy (1), complimentary medicine (2) and two not restricted to a clinical topic. Quality of the 7 studies was assessed by a risk of bias tool and was found to be variable. Adherence to the CONSORT harms criteria reported in the 7 studies was inadequate and variable across the items in the checklist. Adverse events are poorly defined, with 6 studies failing to exceed 50% adherence to the items in the checklist. CONCLUSIONS: Readers of RCT publications need to be able to balance the trade-offs between benefits and harms of interventions. This systematic review suggests that this is compromised due to poor reporting of harms which is evident across a range of clinical areas. Improvements in quality could be achieved by wider adoption of the CONSORT harms criteria by journals reporting RCTs.
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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 | 0.667 | 0.841 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.022 | 0.039 |
| Bibliometrics | 0.026 | 0.025 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".