Benefit–risk assessment and reporting in clinical trials of chronic pain treatments: IMMPACT recommendations
Bibliographic record
Abstract
ABSTRACT: Chronic pain clinical trials have historically assessed benefit and risk outcomes separately. However, a growing body of research suggests that a composite metric that accounts for benefit and risk in relation to each other can provide valuable insights into the effects of different treatments. Researchers and regulators have developed a variety of benefit-risk composite metrics, although the extent to which these methods apply to randomized clinical trials (RCTs) of chronic pain has not been evaluated in the published literature. This article was motivated by an Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials consensus meeting and is based on the expert opinion of those who attended. In addition, a review of the benefit-risk assessment tools used in published chronic pain RCTs or highlighted by key professional organizations (ie, Cochrane, European Medicines Agency, Outcome Measures in Rheumatology, and U.S. Food and Drug Administration) was completed. Overall, the review found that benefit-risk metrics are not commonly used in RCTs of chronic pain despite the availability of published methods. A primary recommendation is that composite metrics of benefit-risk should be combined at the level of the individual patient, when possible, in addition to the benefit-risk assessment at the treatment group level. Both levels of analysis (individual and group) can provide valuable insights into the relationship between benefits and risks associated with specific treatments across different patient subpopulations. The systematic assessment of benefit-risk in clinical trials has the potential to enhance the clinical meaningfulness of RCT results.
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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 | 0.509 | 0.747 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.013 | 0.029 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.015 | 0.019 |
| Open science | 0.021 | 0.013 |
| Research integrity | 0.044 | 0.042 |
| Insufficient payload (model declined to judge) | 0.012 | 0.016 |
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".