Obstetrical and neonatal outcomes in obese parturients
Bibliographic record
Abstract
OBJECTIVE: Obstetrical risk is increased with maternal obesity. This prospective study was designed to simultaneously evaluate the outcomes in obese parturients and their newborns. METHODS: Patients with a body mass index (BMI) > or =35 were prospectively identified and compared to an equal number of normal weight parturients. Maternal and neonatal outcome measures were compared for the peripartum and neonatal period. RESULTS: We identified 580 obese parturients over a 6 month period and compared them to an equal number of normal weight parturients. The incidence of obesity in this population was 23%. Obesity was associated with increased rates of hypertension, diabetes, and cesarean section. Obese patients were more likely to develop postpartum complications. Neonatal outcomes were compared for infants > or =37 weeks gestation excluding multiple births (496 neonates in the obese group and 520 in the control group). The neonates of obese parturients were more likely to be macrosomic, have 1-minute Apgar scores of < or =7.0 and require admission to a special care unit. Sub-group analysis showed that negative outcomes for parturients and their neonates correlated with increasing BMI. Neonates born to obese diabetic parturients had the highest risk of poor outcomes. CONCLUSIONS: Maternal obesity confers increased risks for both the parturient and their newborn.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".