What Are the Top Benefits of a Prenatal Diagnosis of Fetal Esophageal or Intestinal Atresia?
Bibliographic record
Abstract
Background: The aim of the study was to investigate how prenatal diagnosis of fetal esophageal or intestinal atresia impacts obstetric and neonatal outcomes. Methods: This was a retrospective cohort study of mothers and their neonates affected by fetal esophageal or intestinal atresia and followed in our center. The study population comprised 29 mothers and their fetuses (57%) identified prenatally, and 22 mothers and their neonates (43%) diagnosed postnatally. Results: There was no significant difference between the two groups in induction of labor or mode of delivery. In the prenatal group, there was significantly higher prevalence of preterm birth before 37 and 34 weeks (59% vs. 31% and 24% vs. 0%, respectively) with no significant differences in rates of hospitalizations in a high-risk maternity unit and severe polyhydramnios (24% vs. 9% and 14% vs. 0%, respectively). Univariate regression analysis demonstrated that the only significant contribution to the prediction of delivery before 37 weeks was provided by prenatal diagnosis (R 2 = 0.08, P = 0.046). Furthermore, we found no differences in age at surgery, neonatal complications and neonatal death. We observed significant differences in the duration of a neonatal intensive care unit stay (12 days (interquartile range: 41) vs. 6 (interquartile range: 4)). Conclusions: We were not able to demonstrate any benefits of a prenatal diagnosis of fetal esophageal or intestinal atresia. This should reassure maternity care providers anytime such an unexpected delivery occurs. J Clin Gynecol Obstet. 2023;12(1):1-7 doi: https://doi.org/10.14740/jcgo833
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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 | 0.007 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".