Genetic and environmental determinants of menstrual characteristics
Bibliographic record
Abstract
BACKGROUND: The impact of women's menstrual cycle on her quality of life, health, work, and community is substantial. Menstrual disturbance is linked with general ill conditions such as migraine, asthma, and endocrinopathies. The clinical significance of medical interventions to prevent these conditions becomes clear if the role of genetic or environment is clarified. AIMS: To identify the genetic and environmental contribution on menstrual characteristics. SETTING AND DESIGN: This was a cross-sectional study in 2 Asian countries. MATERIALS AND METHODS: 2 cohorts of monozygotic and dizygotic twins born between (1945-1988, n = 122) and (1951-1993, n = 71) were taken. A standard questionnaire was designed inclusive of socio- demographic characteristics of subjects as well as menstrual history (duration, interval, amount, irregularity). Subjects were interviewed by phone. STATISTICAL ANALYSIS: Quantitative variables were analyzed using Falconars' formula as well as maximum likelihood analysis. Structural modeling was then applied to twin correlations to provide estimates of the relative genetic and/or environmental factors contribution in determining the measured trait. RESULTS: Menstrual characteristics were found to be under environmental influence where the best fitting model for menstrual interval and duration was common environment. CDF plotting confirmed the results for both variables. Proband-wise concordance analysis for amount of menstruation, amenorrhea, and irregular menstruation revealed no genetic influence. The best fitting model for menstrual irregularity was CE (C73%, E27%). The same model was defined for amenorrhea (C48%, E52%). CONCLUSIONS: Environmental factors are most likely responsible to determine the menstrual flow, its integrity, and regularity. These factors need to be studied further.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".