Small‐for‐gestational‐age birth and maternal plasma antioxidant levels in mid‐gestation: a nested case–control study
Bibliographic record
Abstract
OBJECTIVE: To assess whether maternal plasma antioxidant levels in mid-pregnancy are associated with small-for-gestational-age (SGA) birth. DESIGN: Case-control study nested within a population-based cohort study. SETTING: Four hospitals in Montreal, Canada. POPULATION: Pregnant women recruited before 24 weeks of gestation, whose pregnancies were not complicated by pre-eclampsia or preterm delivery. METHODS: Blood samples were obtained at 24-26 weeks and assayed for nutritionally derived antioxidant levels in SGA cases (n = 324) and randomly selected controls with birthweights between the 25th and 75th centiles (n = 672). We performed logistic regression analyses using the standardised z-score of each antioxidant as the main independent variable, after summing highly correlated antioxidants or combining via principle component analysis. We adjusted for risk factors for SGA that were associated with antioxidant levels. MAIN OUTCOME MEASURES: SGA, birthweight <10th centile for gestational age and sex. RESULTS: Retinol was positively associated with risk of SGA (adjusted odds ratio [OR] 1.41; 95% confidence interval [95% CI] 1.22-1.63, per SD increase). Carotenoids (log of the sum of β-carotene, lutein/zeaxanthin, α- and β-cryptoxanthin) were negatively associated with SGA (adjusted OR 0.64; 95% CI 0.54-0.78, per SD increase). We found no significant associations between SGA and lycopene or any of the forms of vitamin E assessed, including α-tocopherol, corrected α-tocopherol (per nmol/l of low-density lipoprotein articles), or γ-tocopherol. CONCLUSIONS: Elevated retinol may be associated with an increased risk of SGA, whereas elevated carotenoid levels may reduce the risk. A better understanding of the nature of these associations is required, however, before recommending specific nutritional interventions in an attempt to prevent SGA birth.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".