Type of infertility and prevalence of congenital malformations
Bibliographic record
Abstract
BACKGROUND: Children conceived with assisted reproductive technologies (ART) or after a long waiting time have a higher prevalence of congenital malformations, but few studies have examined the contribution of type of infertility. OBJECTIVES: To quantify the association between causes of infertility and prevalence of malformations. METHODS: We compared the prevalence at birth of all and severe malformations diagnosed up to age 2 between 6656 children born in 1996-2017 to parents who had previously been assessed for infertility a an academic fertility clinic ("exposed") and 10,382 children born in the same period to parents with no recent medical history of infertility ("reference"). We estimated prevalence ratios (PR) and prevalence differences (PD), by infertility status, type of treatment (non-ART, ART), and infertility diagnosis, in all children and among singletons. RESULTS: Compared with children of parents with no infertility, children of parents with infertility had a higher prevalence of malformations (both definitions), particularly following ART conceptions. After accounting for treatment, ovulatory disorders were associated with a higher prevalence of both all (PR 1.49, 95% confidence interval (CI) 1.15, 1.93; PD 3.8, 95% CI 1.0, 6.6) and severe (PR 1.53, 95% CI 1.02, 2.29; PD 1.8, 95% CI -0.2, 3.7) malformations (the estimates refer to exposed children conceived without treatment). Unexplained and male factor infertility were associated with all and severe malformations, respectively. Estimates among singletons were similar. A diagnosis of ovulatory disorders was associated with all malformations also in analyses restricted to exposed children, regardless of treatment (we did not examine severe malformations, due to limited power). CONCLUSIONS: In this study, ovulatory disorders were consistently associated with a higher prevalence of congenital malformations (including severe malformations) among live births, regardless of mode of conception.
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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 | 0.005 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".