Considerations for improving assay sensitivity in chronic pain clinical trials: IMMPACT recommendations
Bibliographic record
Abstract
A number of pharmacologic treatments examined in recent randomized clinical trials (RCTs) have failed to show statistically significant superiority to placebo in conditions in which their efficacy had previously been demonstrated. Assuming the validity of previous evidence of efficacy and the comparability of the patients and outcome measures in these studies, such results may be a consequence of limitations in the ability of these RCTs to demonstrate the benefits of efficacious analgesic treatments vs placebo ("assay sensitivity"). Efforts to improve the assay sensitivity of analgesic trials could reduce the rate of falsely negative trials of efficacious medications and improve the efficiency of analgesic drug development. Therefore, an Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials consensus meeting was convened in which the assay sensitivity of chronic pain trials was reviewed and discussed. On the basis of this meeting and subsequent discussions, the authors recommend consideration of a number of patient, study design, study site, and outcome measurement factors that have the potential to affect the assay sensitivity of RCTs of chronic pain treatments. Increased attention to and research on methodological aspects of clinical trials and their relationships with assay sensitivity have the potential to provide the foundation for an evidence-based approach to the design of analgesic clinical trials and expedite the identification of analgesic treatments with improved efficacy and safety.
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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 | 0.803 | 0.890 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.014 | 0.021 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.006 | 0.028 |
| Scholarly communication | 0.022 | 0.026 |
| Open science | 0.023 | 0.011 |
| Research integrity | 0.075 | 0.057 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".