Is <scp>HbA1c</scp> associated with birth weight? A multivariable analysis of Pakistani pregnant women
Bibliographic record
Abstract
AIM: Globally, one in seven infants is born with low birth weight and 3%-7% of infants are born with high birth weight, with the greatest burden noted in low- and middle-income countries. This study investigated the association between maternal prenatal glucose regulation and birth weight and the moderating effect of fetal sex among Pakistani women. METHODS: Secondary data from a prospective longitudinal study of healthy pregnant women from Pakistan (N = 189) was used. Participants provided a blood sample (12-19 weeks' gestational age) for the assessment of HbA1c (%). Birth weight (g) was collected following delivery. RESULTS: Higher maternal HbA1c was associated with higher birth weight (b = 181.81, t[189] = 2.15, p = 0.03), which was moderated by fetal sex (b = -326.27, t[189] = -2.47, p = 0.02), after adjusting for gestational age at birth, ethnicity, and pregnancy weight. Among women carrying a male fetus, every 1% increase in HbA1c predicted a 182 g increase in birth weight (b = 181.81, t[189] = 2.15, p = 0.03). CONCLUSIONS: Results extend research from high-income countries and indicate that fetal sex may have implications for glucose regulation in early to mid-pregnancy. Future research should examine sociocultural factors, which could elucidate potential mediating factors in the relation between HbA1c and birth weight in healthy pregnancies.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".