The cumulative impact of parity on the body mass index (BMI) in a non-selected Lower Saxony population
Bibliographic record
Abstract
OBJECTIVES: During the last decade obesity has been continuously rising in adults in industrial countries. The increased occurrence of perinatal complications caused by maternal obesity poses a major challenge for obstetricians during pregnancy and childbirth. This study aims to examine the association between parity, pregnancy, birth risks, and body mass index (BMI) of women from Lower Saxony, Germany. METHODS: ). RESULTS: Most of the mothers in this study population were either in their first (33.9%) or second pregnancy (43.4%). The mean age of women giving birth for the first time was 28.3 years. Maternal age increased with increasing parity. The proportion of pregnant women with a BMI over 30 was 11% in primiparous women, 14.3% in second para, 17.3% in third para and 24.1% in fourth para or more women. Increasing parity was positively correlated with the incidence of classical diseases related to obesity, namely diabetes mellitus, gestational diabetes, hypertension, pregnancy-related hypertension and urinary protein excretion. An increased risk of primary or secondary cesarean section was observed in the obese women, particularly during the first deliveries. CONCLUSIONS: There is a positive and significant correlation between parity and increased maternal BMI. The highest weight gain happens during the first pregnancy. The rate of operative deliveries and complications during delivery is increased in obese pregnant women.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".