Relationship between adaptation and cardiovascular response to tonic cold and heat pain Adaptability to tonic pain and cardiovascular responses
Bibliographic record
Abstract
BACKGROUND: The mechanisms of adaptation to tonic pain are not elucidated. We hypothesized that the adaptability to tonic pain is related to the cardiovascular system. METHODS: Twenty-six subjects received over two sessions in a random order: tonic cold (7 ± 0.2 °C) and heat pain (47.5 ± 0.5 °C) on the hand for 5 min. Pain intensity, blood pressure (BP), and heart rate (HR) were continuously monitored. RESULTS: Pain experience during the heat (HIT) and cold (CIT) immersion tests exhibited different average time courses, being approximated with a linear and cubic function, respectively. In each test, two groups of participants could be identified based on the time course of their tonic thermal pain: one-third of participants were pain adaptive and two-thirds non adaptive. The adaptive group exhibited higher initial pain, lower last pain, and shorter latency to peak pain than the non-adaptive one. Interestingly, some participants were adaptive to both pain stimuli, most were not. HIT as well as CIT produced a stable elevation of BP. However, BP was higher during CIT than HIT (p = 0.034). HR was also increased during CIT and HIT, but the two tests differed with respect to the time course of responses. Finally, the intensity and time course of pain rating to both HIT and CIT correlated with neither BP nor HR responses. CONCLUSIONS: These results suggest that individual sensitivity and adaptability to tonic thermal pain is related to the intensity of initial pain rating and the latency to peak pain but not to cardiovascular responses.
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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 | 0.000 | 0.002 |
| 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) | 0.002 | 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".