CONSISTENCY AND VARIABILITY IN EXPERIMENTAL HEAT PAIN RESPONSES OVER TIME
Bibliographic record
Abstract
Longitudinal and interventional pain studies require reliable outcome measures. Self-reported pain intensity and phasic heat pain (PHP) unpleasantness are widely used gold-standard measures. Secondary hyperalgesia area elicited by PHP is increasingly used as an outcome measure in experimental pain studies. However, the test-retest reliability and stability of these measures across multiple sessions remain unclear. Twenty-four healthy participants (12 males, 12 females) attended three sessions, each including a familiarization procedure and a PHP calibrated to 50/100 pain intensity. Pain intensity ratings, pain unpleasantness ratings, and the area of secondary hyperalgesia were recorded after PHP. Intraclass correlation coefficients (ICC) indicated good reliability for pain intensity (ICC = 0.755) and secondary hyperalgesia (ICC = 0.840), and moderate reliability for pain unpleasantness (ICC = 0.636). Repeated-measures ANOVA revealed low stability for self-reported pain intensity and unpleasantness across sessions (ps < 0.005), whereas secondary hyperalgesia showed high stability (p = 0.296) supported by Bayesian analysis (BF01 = 3.36). Validation in an independent dataset confirmed moderate reliability for PHP pain intensity (ICC = 0.718). These findings suggest that while all PHP outcome measures show moderate-to-good reliability, only secondary hyperalgesia area remains stable across sessions, highlighting its potential as a robust measure in longitudinal and interventional pain research.
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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 | 0.014 | 0.031 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".