Autonomic responses to heat pain: Heart rate, skin conductance, and their relation to verbal ratings and stimulus intensity
Bibliographic record
Abstract
In human pain experiments, as well as in clinical settings, subjects are often asked to assess pain using scales (eg, numeric rating scales). Although most subjects have little difficulty in using these tools, some lack the necessary basic cognitive or motor skills (eg, paralyzed patients). Thus, the identification of appropriate nonverbal measures of pain has significant clinical relevance. In this study, we assessed heart rate (HR), skin conductance (SC), and verbal ratings in 39 healthy male subjects during the application of twelve 6-s heat stimuli of different intensities on the subjects' left forearm. Both HR and SC increased with more intense painful stimulation. However, HR but not SC, significantly correlated with pain ratings at the group level, suggesting that HR may be a better predictor of between-subject differences in pain than is SC. Conversely, changes in SC better predicted variations in ratings within a given individual, suggesting that it is more sensitive to relative changes in perception. The differences in findings derived from between- and within-subject analyses may result from greater within-subject variability in HR. We conclude that at least for male subjects, HR provides a better predictor of pain perception than SC, but that data should be averaged over several stimulus presentations to achieve consistent results. Nevertheless, variability among studies, and the indication that gender of both the subject and experimenter could influence autonomic results, lead us to advise caution in using autonomic or any other surrogate measures to infer pain in individuals who cannot adequately report their perception. Skin conductance is more sensitive to detect within-subject perceptual changes, but heart rate appears to better predict pain ratings at the group level.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".