Bibliographic record
Abstract
Several pain conditions localized to the craniofacial region show a remarkable sex-related difference in their prevalence. These conditions include temporomandibular disorders and burning mouth syndrome as well as tension-type, migraine, and cluster headaches. The mechanisms that underlie sex-related differences in the prevalence of these craniofacial pain conditions remain obscure and likely involve both physiological and psychosocial factors. In terms of physiological factors relevant to the development of headache, direct evidence of sex-related differences in the properties of dural afferent fibers or durally activated second-order trigeminal sensory neurons has yet to be provided. There is, however, evidence for sex-related differences in the response properties of afferent fibers and second-order trigeminal sensory neurons that convey nociceptive input from other craniofacial tissues associated with sex-related differences in chronic pain conditions, such as those that innervate the masseter muscle and temporomandibular joint. Further, modulation of craniofacial nociceptive input by opioidergic receptor mechanisms appears to be dependent on biological sex. Research into mechanisms that may contribute to sex-related differences in trigeminal nociceptive processing has primarily focused on effect of the female sex hormone estrogen, which appears to alter the excitability of trigeminal afferent fibers and sensory neurons to noxious stimulation of craniofacial tissues. This article discusses current knowledge of potential physiological mechanisms that could contribute to sex-related differences in certain craniofacial pain conditions.
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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) | 0.004 | 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".