Small-for-gestational age and its association with maternal blood glucose, body mass index and stature: a perinatal cohort study among Chinese women
Bibliographic record
Abstract
OBJECTIVE: To examine whether maternal low blood glucose (BG), low body mass index (BMI) and small stature have a joint effect on the risk of delivery of a small-for-gestational age (SGA) infant. DESIGN: Women from a perinatal cohort were followed up from receiving perinatal healthcare to giving birth. SETTING: Beichen District, Tianjin, China between June 2011 and October 2012. PARTICIPANTS: 1572 women aged 19-39 years with valid values of stature, BMI and BG level at gestational diabetes mellitus screening (gestational weeks 24-28), glucose challenge test <7.8 mmol/L and singleton birth (≥37 weeks' gestation). MAIN OUTCOME MEASURES: SGA was defined as birth weight <10th centile for gender separated gestational age of Tianjin singletons. RESULTS: 164 neonates (10.4%) were identified as SGA. From multiple logistic regression models, the ORs (95% CI) of delivery of SGA were 0.84 (0.72 to 0.98), 0.61 (0.49 to 0.74) and 0.64 (0.54 to 0.76) for every 1 SD increase in maternal BG, BMI and stature, respectively. When dichotomises, maternal BG (<6.0 vs ≥6.0 mmol/L), BMI (<24 vs ≥24 kg/m(2)) and stature (<160.0 vs ≥160.0 cm), those with BG, BMI and stature all in the lower categories had ∼8 times higher odds of delivering an SGA neonate (OR (95% CI) 8.01 (3.78 to 16.96)) relative to the reference that had BG, BMI and stature all in the high categories. The odds for an SGA delivery among women who had any 2 variables in the lower categories were ∼2-4 times higher. CONCLUSIONS: Low maternal BG is associated with an increased risk of having an SGA infant. The risk of SGA is significantly increased when the mother is also short and has a low BMI. This may be a useful clinical tool to identify women at higher risk for having an SGA infant at delivery.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".