To know or not to know: Effect of third‐trimester sonographic fetal weight estimation on outcomes of large‐for‐gestational age neonates
Bibliographic record
Abstract
Abstract Objective The aim of the present study was to evaluate the impact of late third‐trimester sonographic estimation of large for gestational age fetuses on pregnancy management and selected fetal and maternal adverse outcomes. Methods A retrospective cohort study was conducted in a tertiary, university‐affiliated medical center between 2015 and 2019. All singleton large‐for‐gestational‐age neonates born during this period were included. The cohort was divided into two groups: neonates for whom fetal weight was estimated on late third trimester (<14 days before delivery) sonography and neonates with no recent fetal weight estimation. The groups were compared for pregnancy management strategies, rates of labor induction, cesarean deliveries, and maternal and neonatal outcomes. Results A total of 1712 neonates were included in the study, among whom 791 (46.2%) had a late third‐trimester fetal weight estimation (study group) and 921 (53.8%) did not (control group). Compared to the control group, the study group was characterized by higher rates of maternal primiparity (24.20% vs 19.20%, P = 0.013), higher maternal body mass index (26.0 ± 6.2 kg/m2 vs 24.7 ± 4.5 kg/m2, P = 0.002), more inductions of labor (29.84% vs 16.40%, P < 0.001) and cesarean deliveries (31.0% vs 19.97%, P < 0.001). There were no clinical differences in neonatal birth weight (4041 ± 256 g vs 3984 264 g, P < 0.001) and no significant differences between other neonatal outcomes, as rates of admission to the neonatal intensive care unit, jaundice, hypoglycemia, and shoulder dystocia. Conclusion Late third‐trimester sonographic fetal weight estimation is associated with a higher rate of labor induction and planned and intrapartum cesarean deliveries. In this retrospective cohort study, those interventions did not lead to reduction in maternal or neonatal adverse outcomes.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".