Staircase-evoked Pain May be More Sensitive Than Traditional Pain Assessments in Discriminating Analgesic Effects
Bibliographic record
Abstract
OBJECTIVES: Analgesic trials often fail to show a significant effect even when medications with known efficacy are tested. This could be attributed to insufficient assay sensitivity of analgesic trials, which may be due, in part, to the insensitivity of pain-related outcome measures. The aim of this methodological study was to assess the responsiveness of evoked pain generated by the staircase procedure compared with other commonly used pain outcomes in knee osteoarthritis. METHODS: This was a randomized, double-blind, placebo-controlled, cross-over trial of 1-week treatment of naproxen versus placebo. Participants were assigned to one of the 2 treatment sequences (naproxen-placebo or placebo-naproxen). Pain-at-rest, evoked pain using the Staircase-Evoked Pain Procedure (StEPP), pain diary, and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) data were collected before and at the end of each treatment sequence. RESULTS: A total of 55 osteoarthritis patients (30 M, 25 F) completed the study. Among all pain assessments, evoked pain was the most sensitive outcome to detect treatment effects, with Standardized Effect Size (SES) of 0.47 followed by the WOMAC and pain-at-rest with SES of 0.43 and 0.36, respectively. Sample size calculations demonstrated that compared with spontaneous pain, the evoked pain model reduces required number of subjects by 40%. DISCUSSION: Study results support our hypothesis that evoked pain using the StEPP may demonstrate greater responsiveness to treatment effects compared with traditional pain-related outcome measures. Accordingly, these results may facilitate development and validation of other chronic pain-related evoked pain models, which could contribute to future research and development of new analgesics.
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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 | 0.075 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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; 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".