Impact of Obstetric Nutritional Risk on Perinatal Morbidity: A Case-Control Study
Bibliographic record
Abstract
Objective. Malnutrition is among the most relevant problems in pregnant women, which affects nutritional status of the fetus and newborn outcome. The impact of obstetric nutritional risk (ONR) on neonatal morbidity has not been investigated. The purpose of this work was to identify a possible association between ONR, on high-risk pregnancy (HRP) patients and perinatal morbidity. Methods. This Case Control study included 118 neonates of HRP patients who were either (Cases, n =66) or not (Controls, n =52) with ONR. Groups were then compared to identify associated Neonatal Morbidity. Study variables included: neonatal morbidity, one and five-minute APGAR scores, birth weight, gestation weeks, preterm births, newborn gender and neonatal complications. Results. Morbidity and preterm births were identified in 40 (60.6%) and 11 (21.1%) neonates (p <0.001); and 40 (60.6%) and 14 (26.9%) neonates (p <0.001), for cases and controls, respectively. Average one-minute and five-minute APGAR scores was 6 ± 1 and 8 ± 1 (p <0.05); and 8 ± 1 and 9 ± 1 (p >0.05) for cases and controls, respectively. Average birth weight and gestation weeks was 2,272.5 and 2,548.5 grams (p <0.05); and 34 ± 3.7 and 37 ± 3 weeks (p <0.05) for cases and controls, respectively. There were 34 (51.51%) and 24 (46.15%) female neonates (p < 0.05); and 32 (48.48%) and 28 (53.85%) male neonates (p >0.05) for cases and control, respectively. Conclusion. Morbidity was significantly higher in neonates of HRP patients with ONR. Therefore, Obstetric Nutritional Risk had a negative impact on perinatal morbidity and newborn outcome.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.018 |
| 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 | 0.001 |
| 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 teacher head, 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".