Minimum clinically important differences in acute pain: a patient-level re-analysis of randomized controlled analgesic trials submitted to the US Food and Drug Administration
Bibliographic record
Abstract
ABSTRACT: The lack of established minimum clinically important differences in acute pain has made it challenging to interpret efficacy in analgesic trials. We performed a patient-level re-analysis of double-blind, placebo-controlled trials submitted to the US Food and Drug Administration to estimate minimum clinically important differences in acute postoperative pain. Trials were categorized by acute surgical pain model: dental extraction, bunionectomy, orthopedic surgery, and soft tissue surgery. Pain intensity was assessed using the 0 to 10 numeric rating scale (NRS) or 0 to 100 visual analog scale, with visual analog scale scores converted to NRS for analysis. To avoid misclassification from arbitrary thresholds on global impression of change or pain relief scales, meaningful pain relief was determined using the double-stopwatch technique, where patients actively indicated the times they experienced perceptible and meaningful relief. Across 29 trials, 9047 patients with moderate-to-severe baseline pain were included. Patients with severe baseline pain (NRS ≥7) reported meaningful relief at a higher absolute NRS and required larger absolute reductions in pain intensity than those with moderate baseline pain (NRS 4-<7). However, the percent reduction in pain at meaningful relief remained stable across baseline pain levels, suggesting patients assess meaningful relief in relative rather than absolute terms. No appreciable differences in the changes in pain at meaningful relief were observed by age, sex, drug, or route of administration. Receiver operating characteristic curve analysis identified a 50% reduction in pain intensity as a consistent and clinically meaningful threshold across surgical pain models, supporting its use as a standardized patient-centric metric for evaluating analgesic efficacy.
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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 | 0.264 | 0.338 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.023 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".