Excitatory and inhibitory pain mechanisms during the menstrual cycle in healthy women
Bibliographic record
Abstract
Sex differences in pain perception have been clearly documented in the literature during the last decades and it has been shown that women perceived more pain than men. Sex hormones (SHs) are thought to be one of the main mechanisms which explain sex differences in pain. Pain is a dynamic phenomenon involving both excitatory and inhibitory mechanisms. Previous studies have verified the effect of SH on excitatory mechanisms but not on endogenous pain inhibitory mechanisms. The main objective of this study was to establish if pain perception and diffuse noxious inhibitory control (DNIC) vary across the menstrual cycle (MC). Thirty-two healthy women with a regular MC were tested three times across their MC (days 1-3, days 12-14 and days 19-23). Experimental pain consisted of two tonic heat pain stimulations (thermode) separated by a 2-min cold pressor test (CPT) (conditioning stimulus activating DNIC). Pain ratings were measured with a visual analogue scale. Heat pain threshold, pain tolerance and mean pain intensity during both the 2-min thermode test and CPT did not vary throughout the MC. However, we found significantly more pain inhibition (DNIC effectiveness) during the ovulatory phase compared to the menstrual and luteal phases (p=0.05). The main finding of this study is the observation that only inhibitory mechanisms (DNIC analgesia) and not excitatory pain mechanisms vary throughout the MC, where women have greater DNIC in the ovulatory phase. The higher occurrence of pain and lower pain threshold previously reported during the MC could be related to a reduction in endogenous pain control mechanisms.
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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) | 0.001 | 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".