Gender‐Based Differences and Menstrual Cycle‐Related Changes in Specific Diseases: Implications for Pharmacotherapy
Bibliographic record
Abstract
Pharmacists should be aware of gender-based differences and menstrual cycle-related changes in six diseases: asthma, arthritis, migraine, diabetes, depression, and epilepsy. In general, women report symptoms of physical illness at higher rates, visit physicians more frequently, and make greater use of other health care services than men. Whereas reasons for these gender differences are not fully clear, a combination of biologic, physiologic, social, behavioral, psychologic, and cultural factors most likely contributes. A significant percentage of women with asthma, arthritis, migraine, diabetes, depression, or epilepsy experience worsening of their disease premenstrually. The mechanism is unknown, but is speculated to be multifactorial because of many endogenous and exogenous modulators and mediators of each disease. As part of general therapy for cycle-related exacerbations of any one of these disorders, patients should be encouraged to use a menstrual calendar to track signs and symptoms for two to three cycles; if cyclic trends are identified, the women should anticipate exacerbations and avoid triggering factors. Cyclic modulation with pharmacotherapy may be attempted. If unsuccessful, a trial of medical ovulation suppression with a gonadotropin-releasing hormone (GnRH) analog may be warranted. If that is successful, continuous therapy with a GnRH analog and steroid add-back therapy or less expensive alternatives may be effective. If pharmacotherapy is impractical, hysterectomy and bilateral oophorectomy with estrogen replacement therapy is a last resort. Gender differences and menstrual cycle-related changes are important areas for clinical and mechanistic research.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".