Ovarian hormones and chronic pain: A comprehensive review
Bibliographic record
Abstract
Most chronic noncancer pain (CNCP) conditions are more common in women and have been reported to worsen, particularly during the peak reproductive years. This phenomenon suggests that ovarian hormones might play a role in modulating CNCP pain. To this end, we reviewed human literature aiming to assess the potential role of ovarian hormones in modulating the following CNCP conditions: musculoskeletal pain, migraine headache, temporal mandibular disorder, and pelvic pain. We found 50 relevant clinical studies, the majority of which demonstrated a correlation between hormone changes or treatments and pain intensity, threshold, or symptoms. Taken together, the findings suggest that changes in hormonal levels may well play a role in modulating the severity of CNCP conditions. However, the lack of consistency in study design, methodology, and interpretation of menstrual cycle phases impedes comparison between the studies. Thus, while the literature is highly suggestive of the role of ovarian hormones in modulating CNCP conditions, serious confounds impede a definitive understanding for most conditions except menstrual migraine and endometriosis. It may be that these inconsistencies and the resulting lack of clarity have contributed to the failure of hormonal effects being translated into medical practice for treatment of CNCP 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".