Influence of intrauterine growth restriction on caesarean delivery risk among preterm pregnancies undergoing induction of labor for hypertensive disease
Bibliographic record
Abstract
AIM: To evaluate the effect of intrauterine growth restriction (IUGR) on the success rate of labor induction of preterm pregnancies complicated by hypertensive disorders. METHODS: A retrospective cohort study conducted using data from the Centers for Disease Control and Prevention's Linked Birth-Infant Death File in the United States from 2009 to 2013. Our cohort included live normal singleton cephalic pregnancies complicated by hypertensive disorders that underwent induction of labor and delivered between 24 and 35.6 weeks' gestation. Study subjects were categorized by the presence or absence of IUGR. Multivariate logistic regression was used to estimate the adjusted effect of IUGR on risk of caesarean deliveries. RESULTS: Of 41 640 births meeting study criteria, 39 890 had no IUGR and 1750 had IUGR infants. The overall caesarean delivery rate was 22.2%, with caesarean delivery risk being higher among pregnancies complicated by IUGR versus those not complicated by IUGR (33.2% vs 21.7%, respectively) (odds ratio 2.00, 95% confidence interval 1.78-2.25). The effect of IUGR on risk of caesarean sections was most pronounced for gestational ages between 28 and 36 weeks. The effect of IUGR was highest among obese women, with the risk of caesarean in IUGR vs non-IUGR pregnancies being 62.8% vs 41.4%, respectively (odds ratio 2.53, 95% confidence interval 1.98-3.24). CONCLUSION: Induction of labor of preterm pregnancy complicated by hypertensive disorders should be considered a reasonable option for delivery; however, in the context of IUGR, women should be informed of the considerable higher risk of caesarean delivery.
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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 | 0.008 |
| 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 | 0.001 |
| 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".