Patterns and prevalence of medication use across the menstrual cycle among healthy, reproductive aged women
Bibliographic record
Abstract
PURPOSE: The purpose of this study is to characterize the patterns of medication intake in healthy, reproductive-age women not using hormonal contraception. METHODS: Two hundered fifty-nine healthy, premenopausal women (18-44 years of age) enrolled in the BioCycle Study (2005-2007) were followed over two menstrual cycles. Women were excluded if they were currently using oral contraceptives or other chronic medications. Over-the-counter and prescription medication use among participants was evaluated daily throughout the study via a diary assessing type of medication, dosage, units, and frequency. Medications were categorized as allergy, antibiotics, central nervous system (CNS), cold and cough, gastrointestinal, musculoskeletal, and pain medication based on primary active ingredient. Medication use within each category was assessed across standardized 28-day cycles to evaluate differences in use across cycle phases (i.e., early, middle, and late). RESULTS: Medication use was reported by 73% of participants. The most and least frequently used medications, respectively, were pain (69%) and musculoskeletal medications (1%). Pain, CNS, and antibiotic medication use varied significantly across the cycle, with pain and CNS medication more frequently reported during menses and antibiotics more frequently during the luteal phase. Allergy, cold and cough, gastrointestinal, and musculoskeletal medication use did not vary across the cycle. CONCLUSIONS: Patterns of medication use among reproductive age women vary across the menstrual cycle for certain types of medications, particularly in pain (e.g., Ibuprofen), antibiotics (e,g, Amoxicillin), and CNS (e.g., Adderall) medications. Future studies involving use of these types of medication in premenopausal women may need to consider the relationship of their use to the menstrual cycle. Copyright © 2016 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".