Adverse event assessment and reporting in trials of newer treatments for post‐operative pain
Bibliographic record
Abstract
BACKGROUND: Assessment and reporting of adverse events (AEs) in studies of perioperative interventions is critical given the potential for unintended and preventable iatrogenic morbidity and mortality. This focused review evaluated the quality of AE assessment and reporting in acute post-operative pain treatment trials. Since older analgesics (e.g., opioids, NSAIDs) already have a well-characterized safety profile, we concentrated on trials of pregabalin and gabapentin as a representative sample of studies where the perioperative safety profile was relatively unknown. METHODS: We reviewed primary reports of trials of pregabalin and gabapentin for treatment of acute post-operative pain for: (1) adherence to the 10 recommendations from the 'CONSORT Extension for Harms,' (2) AE assessment method, (3) timing of AE assessment and reporting, and (4) assessment and reporting of AE severity. RESULTS: We identified 31 trials of pregabalin and 59 of gabapentin. The median number of CONSORT harms recommendations that were satisfied was 7 of 10. The most common (41%) method of AE assessment was direct questioning about specific AEs by investigators. However, AE assessment method was not described in 18% of trials. AE assessments were reported for specified perioperative time points in only 24% of trials. Of greatest concern, no AE data were reported whatsoever in 8 of the included publications. CONCLUSIONS: Considerable widespread improvements are needed in AE reporting for post-operative pain treatment trials. In addition to heightened awareness among clinical investigators, mandatory journal editorial policies may further facilitate improvements in safety assessment and 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 | 0.722 | 0.815 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.020 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.007 | 0.008 |
| 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".