Ovarian Stimulators, Intrauterine Insemination, and Assisted Reproductive Technologies Use and the Risk of Major Congenital Malformations—The AtRISK Study
Bibliographic record
Abstract
OBJECTIVE: To quantify the risk of major congenital malformations (MCMs) associated with the use of ovarian stimulators alone, intrauterine insemination (IUI), and assisted reproductive technologies (ARTs). METHODS: We conducted a case-control analysis using a birth cohort, built with the linkage of data obtained by a self-administered questionnaire, medical, pharmaceutic, and birth databases. Cases were pregnancies with at least one live birth with an MCM. Controls were pregnancies that did not result in major or minor congenital malformations. Multiple logistic regression models were used to calculate the odds ratios (ORs) and confidence intervals (CIs). RESULTS: Among the 5021 pregnancies identified, 825 were cases of MCM and 4196 were controls. Compared with spontaneous conception, the use of ART increased the risk of major urogenital malformations (adjusted OR, 3.11; 95% CI, 1.33-7.27). The use of IUI was associated with an increased risk of major musculoskeletal malformations (adjusted OR, 2.02; 95% CI, 1.10-3.71). Among the 471 women who used fertility treatments for conception, the use of ART was associated with an increased risk of any MCM (adjusted OR, 1.66; 95% CI, 1.00-2.79) and urogenital malformations (adjusted OR, 7.18; 95% CI, 1.59-32.53) when compared with ovarian stimulators used alone. CONCLUSIONS: The use of ART and IUI was associated with an increased risk of major musculoskeletal and urogenital malformations. ART was associated with a higher risk of MCM compared to ovarian stimulators used alone. Even the adjustment, a contribution of the underlying subfertility problems cannot completely ruled out given the differences in the severity of subfertility.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".