Adverse event assessment, analysis, and reporting in recent published analgesic clinical trials: ACTTION systematic review and recommendations
Bibliographic record
Abstract
The development of valid and informative treatment risk-benefit profiles requires consistent and thorough information about adverse event (AE) assessment and participants' AEs during randomized controlled trials (RCTs). Despite a 2004 extension of the Consolidated Standards of Reporting Trials (CONSORT) statement recommending the specific AE information that investigators should report, there is little evidence that analgesic RCTs adequately adhere to these recommendations. This systematic review builds on prior recommendations by describing a comprehensive checklist for AE reporting developed to capture clinically important AE information. Using this checklist, we coded AE assessment methods and reporting in all 80 double-blind RCTs of noninvasive pharmacologic treatments published in the European Journal of Pain, Journal of Pain, and PAIN® from 2006 to 2011. Across all trials, reports of AEs were frequently incomplete, inconsistent across trials, and, in some cases, missing. For example, >40% of trials failed to report any information on serious adverse events. Trials of participants with acute or chronic pain conditions and industry-sponsored trials typically provided more and better-quality AE data than trials involving pain-free volunteers or trials that were not industry sponsored. The results of this review suggest that improved AE reporting is needed in analgesic RCTs. We developed an ACTTION (Analgesic, Anesthetic, and Addiction Clinical Trial Translations, Innovations, Opportunities, and Networks) AE reporting checklist that is intended to assist investigators in thoroughly and consistently capturing and reporting these critically important data in publications.
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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 | 0.428 | 0.665 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.010 | 0.018 |
| Bibliometrics | 0.028 | 0.021 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.009 | 0.007 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".