Caffeine intake and small‐for‐gestational‐age birth: modifying effects of xenobiotic‐metabolising genes and smoking
Bibliographic record
Abstract
The relationship between caffeine consumption and small-for-gestational-age (SGA) birth remains uncertain. However, factors that can influence caffeine metabolism, such as genetic polymorphisms, have not been considered, while other similar factors such as smoking and ethnicity have not always been fully accounted for in the interpretation of results. A case-control study was carried out comprising 493 cases and 472 controls. Cases were newborns whose birthweight was below the 10th percentile according to gestational age and sex, based on national norms, and controls were at or above the 10th percentile. Caffeine consumption from beverages was estimated for each pregnancy trimester. Maternal and newborn variants in the CYP1A2 and CYP2E1 genes involved in the metabolism of caffeine were determined. Contrasting consumption >or=300 mg/day with a lower level, or using caffeiwne as a continuous measure, while adjusting for smoking and nausea, showed no increased risk for SGA. However, when stratifying for cigarette smoking, caffeine odds ratios (for the continuous and dichotomous measures) in the first trimester were statistically heterogeneous, suggesting a greater risk among non-smokers. Using birthweight as the outcome and caffeine as a continuous measure, a small 38 g [95% confidence interval -68, -8] decrement for every 100 mg of daily caffeine was observed in the third trimester. The studied polymorphisms did not modify the effect of caffeine. Caffeine consumption is unlikely to be a major risk factor for SGA or low birthweight in pregnant women.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".