Validation of the integral of pain relief as a measure of chronic pain treatment efficacy
Bibliographic record
Abstract
Abstract Introduction: Several scales are currently in use to measure chronic pain and its variations with treatments, as patient-reported outcomes; however, they are based only on pain intensity and neglect the duration of pain relief, critical to compare the efficacy of different treatments. Objectives: To assess the validity of the integral of pain relief (IPR), an index that combines the percentage and duration of pain relief, to compare the efficacy of various treatments. Methods: Patients undergoing intra-articular corticosteroid injections for knee osteoarthritis were recruited to validate the IPR. The pain intensity was assessed at baseline, using 2 different pain scales; and at follow-up visits 2, 4, 6, 8, and 12 weeks after the injection; adding the IPR and the patient's global impression of change (PGIC). The correspondence between the various scales and PGIC values was examined with data stratification, receiver operating characteristic curve analysis, and Spearman correlations. Results: In total, 66 patients were included in the analysis (20 men and 46 women, mean age 68.5 ± 9.2 years). All measures were strongly correlated, and the IPR could effectively identify responders at weeks 8 and 12, according to PGIC values, with a single value that can also be used for statistical analyses, unlike the PGIC. In addition, the IPR showed significant sex differences, with more effective and prolonged pain relief in women than in men ( P = 0.042). Conclusion: The IPR demonstrated validity and responsiveness to assess the efficacy and duration of pain relief treatments in patients with 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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".