Unintended pregnancies and exposure to potential human teratogens
Bibliographic record
Abstract
BACKGROUND: We aimed to investigate the risk factors associated with unintended pregnancies as well as the association between unintended pregnancies and potential teratogenic exposures. METHODS: A cross-sectional survey was performed among women attending the Maternity School of the Samsung Cheil Hospital and Women's Health Care Center in Seoul, Korea. Demographic data, obstetric history, socioeconomic status, intention to become pregnant, and exposure to potential teratogens were obtained. RESULTS: A total of 1354 women with median age of 29 years and median gestational age of 29 weeks were included. Of these, an educational level above high school was 74.2%, primigravida was 77.3% and unintended pregnancy was 48%. In the logistic regression analysis, women younger than 24 years of age had a relative risk (RR) of 2.5 (95% confidence interval [CI], 1.3-4.7) of having an unintended pregnancy and women with lower household monthly income level had a RR of 1.3 (95% CI, 1.0-1.6). Women with unintended pregnancies had an RR of 1.9 (95% CI, 1.5-2.5), 3.0 (95% CI, 2.0-4.5), 1.5 (95% CI, 1.0-2.3), 2.9 (95% CI, 1.1-7.2), and 2.0 (95% CI, 1.62.4) of being exposed to alcohol, medications, cigarette smoking, X-rays, or to any of these, respectively, during the first trimester of pregnancy. CONCLUSIONS: Unintended pregnancies are more likely to occur among young women with a lower household monthly income level. Prenatal counseling should be especially recommended for women with unintended pregnancies in order to evaluate whether they have been exposed to potential teratogenic agents.
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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 | 0.003 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".