Noxious heat evokes stronger sharp and annoying sensations in women than men in hairy skin but not in glabrous skin
Bibliographic record
Abstract
Brief noxious heat evokes more intense pain in women than in men; however, sex differences in the intensity of pain sensations evoked in hairy and glabrous skins are not clearly understood. Glabrous skin putatively lacks the type of A-delta nociceptors that underlie heat-evoked sharp sensation. Therefore, we assessed whether noxious heat-evoked pain qualities differed for hairy and glabrous skins and whether sex differences exist in these evoked pains. We applied a prolonged (30s) ramped noxious heat stimulus to the dorsal and ventral aspects of the feet of 16 males and 16 females. Stimuli were calibrated in each subject to evoke a peak pain magnitude of 50/100. Subjects provided continuous online ratings of pain, annoyance, burning, sharp, stinging and cutting sensations in separate runs. The results indicate that both sex and skin type impact noxious heat-evoked sensations. Specifically, ratings of sharp sensations and annoyance evoked in hairy skin were significantly more intense in women than in men. Sharp, stinging and cutting sensations were evoked in glabrous skin, but the magnitude of these sensations was greater in hairy skin than glabrous skin; an effect only in females. Also, there was no sex difference in sharp sensation and annoyance in glabrous skin. These findings suggest that sharp sensations are evoked more prominently in hairy than in glabrous skin of women and that sharp sensations and annoyance play a prominent role in mediating aspects of pain-evoked from hairy skin in women.
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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 | 0.001 |
| 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) | 0.009 | 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".