Nonhormonal drug use and its relation to androgens in perimenopausal women
Bibliographic record
Abstract
OBJECTIVE: To outline the prevalence of nonhormonal drug use in middle-aged women and to assess plausible associations between serum androgen levels and variables associated to health such as drug use and planned visits to healthcare units. METHODS: This was a population-based study of women aged 50 to 59 years (n = 6,893). Women were divided into three groups according to their menopause status: premenopausal (PM), postmenopausal without hormone therapy (PM0), and postmenopausal with hormone therapy (PMT). Data regarding current drug use and healthcare visits were collected from a questionnaire. RESULTS: The overall prevalence of nonhormonal drug use was 36.4% in all women. In the PM, PM0, and PMT groups, these percentages were 28.3%, 35.3%, and 39.3%, respectively, and the differences between them were statistically significant (P < 0.01). In all women, the most common medication used was for cardiovascular conditions (12.0%), followed by those for asthma (4.0%) and pain (3.7%). The number of drugs used by all women and women in the PM0 and the PMT groups were negatively associated with the serum levels of androstenedione (P < 0.05). In the postmenopausal groups, the number of visits to healthcare units was negatively associated to the levels of serum testosterone and androstenedione (P < 0.05). CONCLUSIONS: Hormone therapy in postmenopausal women seems to be associated with increased use of nonhormonal pharmacotherapy, rendering higher prevalence of such drugs in middle-aged women. Postmenopausal women with lower serum testosterone and a higher number of office visits used medications for cardiovascular problems and depression more than other medications. Whether this is an effect related to the hormone therapy itself or to experiencing more perimenopausal symptoms in this group of women is still unclear.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".