The Prevalence and Causes of Primary Infertility in Iran: A Population-Based Study
Bibliographic record
Abstract
BACKGROUND: Primary infertility is a health issue among women over the world. The aim of this study was to investigate the prevalence and causes of primary infertility based on a population-based study in an urban area of Iran. MATERIALS AND METHODS: In a cross-sectional study, a total of 1067 married women who participated in the Tehran Lipid and Glucose Study were randomly selected using systematic random sampling. Unmarried women, those with unwilling pregnancy and duration of marriage below one year were excluded from the study. Data was collected by using validated ad-hoc questionnaires. Descriptive and inferential statistics were used for data analysis. RESULTS: The mean (SD) of age and marriage age of the studied women were 40.3 (9.3) and 20.6 (4.49) years, respectively; the overall prevalence of lifetime primary infertility among couples was 17.3% (185/1067). Ovulatory disorder (39.7%) and male factors (29.1%) were the main causes of primary infertility. In addition, 31 (17%) of the women were diagnosed with more than one cause. According to the logistic regression analysis, primary infertility was independently related to the old age of women (OR: 1.37; 95% CI: 1.14-13.63, P.value: 0.001), higher BMI (OR: 1.95; 95% CI: 1.87-4.14, P.value: 0.003), active smoking (OR: 1.47; 95% CI: 1.38-3.53, P.value: 0.012) and higher educational level (OR: 2.23; 95% CI: 1.12-5.53, P.value: 0.03). CONCLUSION: The prevalence of primary infertility in Iran was higher than the worldwide trends of infertility, indicating that understanding such risks help healthcare providers and policy makers to design and implement interventions to slow down this trend.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".