Research design considerations for confirmatory chronic pain clinical trials: IMMPACT recommendations
Bibliographic record
Abstract
There has been an increase in the number of chronic pain clinical trials in which the treatments being evaluated did not differ significantly from placebo in the primary efficacy analyses despite previous research suggesting that efficacy could be expected. These findings could reflect a true lack of efficacy or methodological and other aspects of these trials that compromise the demonstration of efficacy. There is substantial variability among chronic pain clinical trials with respect to important research design considerations, and identifying and addressing any methodological weaknesses would enhance the likelihood of demonstrating the analgesic effects of new interventions. An IMMPACT consensus meeting was therefore convened to identify the critical research design considerations for confirmatory chronic pain trials and to make recommendations for their conduct. We present recommendations for the major components of confirmatory chronic pain clinical trials, including participant selection, trial phases and duration, treatment groups and dosing regimens, and types of trials. Increased attention to and research on the methodological aspects of confirmatory chronic pain clinical trials has the potential to enhance their assay sensitivity and ultimately provide more meaningful evaluations of treatments for chronic pain.
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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 | 0.647 | 0.801 |
| Meta-epidemiology (narrow) | 0.005 | 0.007 |
| Meta-epidemiology (broad) | 0.012 | 0.021 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.017 | 0.006 |
| Research integrity | 0.052 | 0.038 |
| Insufficient payload (model declined to judge) | 0.010 | 0.013 |
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".