<i>In Vitro</i> Fertilization is Associated with an Increased Risk for Preeclampsia
Bibliographic record
Abstract
OBJECTIVE: To assess the association of intrauterine insemination, in vitro fertilization (IVF) and ovulation induction with the risk of preeclampsia. METHODS: We conducted a population based retrospective cohort study of pregnancies conceived by assisted reproductive technology (1357 exposure subjects, 5190 controls) based on 2005 Niday Perinatal Database for Ontario, Canada. All pregnancies conceived by assisted reproductive technology were identified as exposure group. Four controls were randomly matched for each exposure subject by maternal age, parity, plurality, and delivery hospital level and residence area. The risks for preeclampsia associated with intrauterine insemination, IVF, and ovulation induction were evaluated through conditional logistic regression models compared with their corresponding controls. RESULTS: With adjustment of maternal age, smoking during pregnancy and initiating time of prenatal care, in vitro fertilization was associated with an increased risk for preeclampsia (OR=1.78, 95% CI: 1.05, 3.06), whereas intrauterine insemination (OR=2.44, 95% CI: 0.74, 8.06) and ovulation induction (OR=1.34, 95% CI: 0.31, 5.75) was not associated with the risk for preeclampsia. CONCLUSION: There was a higher incidence of preeclampsia among pregnancies conceived by IVF, but no significant association was found in intrauterine insemination and ovulation induction.
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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 | 0.000 | 0.002 |
| 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) | 0.001 | 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".