Research approaches for evaluating opioid sparing in clinical trials of acute and chronic pain treatments: Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials recommendations
Bibliographic record
Abstract
ABSTRACT: Randomized clinical trials have demonstrated the efficacy of opioid analgesics for the treatment of acute and chronic pain conditions, and for some patients, these medications may be the only effective treatment available. Unfortunately, opioid analgesics are also associated with major risks (eg, opioid use disorder) and adverse outcomes (eg, respiratory depression and falls). The risks and adverse outcomes associated with opioid analgesics have prompted efforts to reduce their use in the treatment of both acute and chronic pain. This article presents Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials (IMMPACT) consensus recommendations for the design of opioid-sparing clinical trials. The recommendations presented in this article are based on the following definition of an opioid-sparing intervention: any intervention that (1) prevents the initiation of treatment with opioid analgesics, (2) decreases the duration of such treatment, (3) reduces the total dosages of opioids that are prescribed for or used by patients, or (4) reduces opioid-related adverse outcomes (without increasing opioid dosages), all without causing an unacceptable increase in pain. These recommendations are based on the results of a background review, presentations and discussions at an IMMPACT consensus meeting, and iterative drafts of this article modified to accommodate input from the co-authors. We discuss opioid sparing definitions, study objectives, outcome measures, the assessment of opioid-related adverse events, incorporation of adequate pain control in trial design, interpretation of research findings, and future research priorities to inform opioid-sparing trial methods. The considerations and recommendations presented in this article are meant to help guide the design, conduct, analysis, and interpretation of future trials.
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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 | 0.871 | 0.884 |
| Meta-epidemiology (narrow) | 0.009 | 0.008 |
| Meta-epidemiology (broad) | 0.017 | 0.026 |
| Bibliometrics | 0.024 | 0.019 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.029 | 0.017 |
| Open science | 0.015 | 0.021 |
| Research integrity | 0.023 | 0.036 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".