Gestational weight gain and fetal growth in underweight women
Bibliographic record
Abstract
BACKGROUND: Despite the current obesity epidemic, maternal underweight remains a common occurrence with potential adverse perinatal outcomes. METHODS: We aimed to investigate the relationship between weight gain during pregnancy, and fetal growth in underweight women with low and late fertility. Women body mass index (BMI), defined according to the World Health Organization's definition, gestational weight gain (GWG), defined by the Institute of Medicine and National Research Council and neonatal birth weight were prospectively collected at maternity ward of Policlinico Abano Terme (Italy) in 793 consecutive at term, uncomplicated deliveries. RESULTS: Among those, 96 (12.1 %) were categorized as underweight (BMI < 18.5 kg/m(2)), 551 (69.5 %) as normal weight, 107 (13.4 %) as overweight, and 39 (4.9 %) as obese, respectively. In all mother groups, GWG was within the range recommended by IOM 2009 guidelines. However, underweight women gained more weight in pregnancy (12.8 ± 3.9 kg) in comparison to normal weight (12.3 ± 6.7 kg) and overweight (11.0 ± 4.7 kg) women and their GWG was significantly higher (p < 0.001) with respect to obese women 5.8 ± 6.1 kg). In addition, offspring of underweight women were comparable in size at birth to offspring of normal weight women, whereas they were significantly lighter to offspring of both overweight and obese women. CONCLUSIONS: Pre-pregnancy underweight does not impact birth weight of healthy, term neonates in presence of normal GWG. Presumably, medical or personal efforts to reach 'optimal' GWG could be a leading choice for many women living in industrialized and in low-income countries.
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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 | 0.000 | 0.003 |
| 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".