Characterizing individual differences in heat-pain sensitivity
Bibliographic record
Abstract
Heat induced pain has been shown to follow a positively accelerating power function for groups of subjects, yet the extent to which this applies to individual subjects is unknown. Statistical methods were developed for assessing the goodness of fit and reliability of the power function for data from individual subjects with the aim of using such functions for characterizing individual differences in heat-pain sensitivity. 175 subjects rated ascending and random series of contact heat stimuli with visual analogue scales for pain intensity (VAS-I) and unpleasantness (VAS-A). Curve fitting showed excellent model fit. Substitution of model estimates in place of observed VAS scores produced minimal bias in group means, about 0.3 VAS units in the ascending series and 1.0 in the random series, on a 0-100 scale. Individual power function exponents were considerably higher for the ascending than for the random series and somewhat higher for VAS-A than for VAS-I (means: ascending VAS-I=9.04, VAS-A=9.80; random VAS-I=4.95, VAS-A=5.67). The reliability of VAS estimates was high (>==.93), and for the ascending series it remained so when extrapolating 4 degrees C beyond the empirical range. Exponent reliability was high for the ascending series (VAS-I=.92; VAS-A=.91), but considerably lower for the random series (VAS-I=.69; VAS-A=.71). Individual differences constituted 60% of the total variance in pain ratings, whereas stimulus temperature accounted for only 40%. This finding underscores the importance of taking individual differences into account when performing pain studies.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".