Maternal waist to hip ratio is a risk factor for macrosomia
Bibliographic record
Abstract
OBJECTIVE: Fetal growth during pregnancy may be affected by the metabolic activity and distribution of fat stores in women. This study investigates the association between waist to hip ratio (WHR) as a measure of the distribution of adiposity in primiparous mothers living in Avon, England, and macrosomia in their offspring. DESIGN: Prospective historical cohort study. SETTING: The Avon Longitudinal Study of Parents and Children (ALSPAC) prospective cohort study in Avon, UK. POPULATION: A cohort of 3083 primiparous women with a term singleton delivery with expected dates of delivery from 1 April 1991 to 31 December 1992. METHODS: The distribution of WHR was categorised into quartiles. We compared the second, third and fourth quartiles against the first (reference) quartile with respect to whether the mother delivered a macrosomic newborn. We controlled for maternal age, gestational age, body mass index (BMI), marital status and racial group using multivariate logistic regression. MAIN OUTCOME MEASURES: Macrosomia defined in three ways: birthweight ≥ 4000 g; birthweight ≥ 4500 g; large for gestational age (LGA: ≥ 95th percentile of birth weight adjusted for sex and gestational age). RESULTS: Waist to hip ratios in the third and fourth quartiles were associated with a higher odds of delivering a macrosomic infant, defined as a birthweight ≥ 4000 g (third quartile, OR 1.59, 95% CI 1.12-2.26; fourth quartile, OR 1.69, 95% CI 1.18-2.42) or as LGA (≥95th percentile of the cohort; third quartile, OR 1.77, 95% CI 1.10-2.85; fourth quartile, OR 1.78, 95% CI 1.09-2.91). When defined as a birthweight ≥ 4500 g, the fourth quartile was associated with increased odds of macrosomia (OR 2.74, 95% CI 1.05-7.16). Odds ratios after adjustment for confounding factors followed a similar pattern. CONCLUSION: Independent of confounding factors, women with increased WHRs were significantly more likely to give birth to macrosomic newborns.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".