Somatic development at birth as influenced by maternal characteristics – an analysis of the German Perinatal Survey
Bibliographic record
Abstract
We investigated the effects of maternal age, body weight, body height, weight gain during pregnancy, smoking during pregnancy, previous live births and being a single mother on somatic development at birth. We analysed data from the German Perinatal Survey for the years 1998-2000 from eight German federal states. We had available data on 508,926 singleton pregnancies and neonates in total; for 508,893 of which we could classify the neonates as small, appropriate or large for gestational age (SGA, AGA or LGA) based on the 10th and 90th birth weight percentiles. Multivariable regression analyses found statistically significant effects of a clinically relevant magnitude for smoking during pregnancy [odds ratio (OR) 2.9 for SGA births for women smoking >10 cigarettes per day], maternal height (OR 1.4 for SGA births for women <162 cm; OR 1.4 for LGA births for women >172 cm), maternal weight (OR 1.5 for SGA births for women <59 kg; OR 1.9 for LGA births for women >69 kg), weight gain during pregnancy (OR 1.9 for SGA births for women with a weight gain <8 kg; OR 2.0 for LGA births for women with a weight gain >18 kg) and previous live births (OR 2.1 for LGA births for women with one or more previous live births). Maternal age and being a single mother also had significant effects but their magnitude was small. Our analysis confirms the clinically relevant effects of smoking, maternal anthropometric measures and weight gain during pregnancy on neonatal somatic development.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".