Effect of vitamin A on the relationship between maternal thyroid hormones in early pregnancy and fetal growth: A prospective cohort study
Bibliographic record
Abstract
Background Fetal growth patterns are influenced by maternal thyroid function and vitamin A level during pregnancy. Vitamin A presents interactions with thyroid tissues and hormonal systems. We examined whether vitamin A status modified the associations of maternal thyroid hormones in early pregnancy and fetal growth outcomes among euthyroid pregnant women in a prospective cohort study (n = 637). Methods We performed multiple linear regression and multinomial logistic regression analysis to investigate the effects of thyroid hormones in early pregnancy on fetal growth according to different levels of serum vitamin A based on median value. Results A 1 pmol/L increase in maternal free triiodothyronine (FT3) levels was associated with an increased birth weight of 0.080 kg (p = 0.023) in women with lower maternal vitamin A levels in early pregnancy. Increased maternal free thyroxine (FT4) was associated with decreased odds for both small size for gestational age (SGA) [odds ratios (OR) = 0.66, 95% confidence interval (CI): 0.45–0.95] and large size for gestational age (LGA) (OR = 0.66, 95% CI: 0.45–0.98) in women with higher vitamin A level in early pregnancy after adjustment for maternal prepregnancy body mass index, gestational weight gain, maternal employed, parity, gestational week at sampling, and gestational diabetes mellitus. Conclusions In Chinese pregnant women without overt thyroid dysfunction, maternal FT4 in early pregnancy was positively associated with optimal fetal growth among women with higher serum vitamin A concentrations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".