Birth risks according to maternal height and weight – an analysis of the German Perinatal Survey
Bibliographic record
Abstract
Objective To investigate the variability in the prevalences of selected birth risks in women of different heights and weights. Methods Data from the German Perinatal Survey of 1998-2000 were analyzed: 503,468 cases contributed to our analysis of the prevalences of selected birth risks specified according to maternal weight groups, 502,562 cases contributed to our analysis according to maternal height groups and 43,928 cases contributed to our analysis of birth risks in women with a body mass index (BMI) of 21-23 kg/m2 but different heights and weights. Data analysis was performed using SPSS version 22. Results Some birth risks varied substantially by maternal height in women with a "normal" BMI of 21-23 kg/m2: the prevalence of post-term birth increased from 8.7% in women with a height of 150 cm to 13.5% in 185 cm tall women, the prevalence of preterm birth decreased from 5.9% (150 cm tall women) to 3.1% (185 cm tall women), a pathologic cardiotocography (CTG) or poor fetal heart sounds on auscultation occurred in 19.4% of the 150 cm tall women but only in 9.2% of 185 cm tall women and cephalopelvic disproportion decreased from 12.3% (150 cm tall women) to 1.2% (185 cm tall women). Analyses of women in different body height and weight groups (without restriction of BMI) likewise showed differences in the prevalences of some birth risks. Conclusion Birth risks may vary by height and weight in women with the same, "normal" BMI. BMI should not be the only way by which the impact of maternal height and weight is assessed with regard to perinatal outcomes such as birth risks.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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".