Maternal ethnicity and risk of neural tube defects: a population-based study
Bibliographic record
Abstract
BACKGROUND: Maternal body mass and the presence of diabetes mellitus are probable risk factors for neural tube defects (NTDs). The association between maternal ethnicity and the risk of NTDs remains poorly understood, however. METHODS: We performed a retrospective population-based study and included all women in Ontario who underwent antenatal maternal screening (MSS) at 15 to 20 weeks' gestation between 1994 and late 2000. Self-declared maternal date of birth, ethnicity and weight and the presence of pregestational diabetes mellitus were recorded in a standardized fashion on the MSS requisition sheet. NTDs were detected antenatally by ultrasonography or fetal autopsy and postnatally by considering all live and stillborn affected infants beyond 20 weeks' gestation. The risk of open NTD was evaluated across the 5 broad ethnic groups used for MSS, with white ethnicity as the referent. RESULTS: Compared with white women (n = 290 799), women of First Nations origin (n = 1551) were at increased associated risk of an NTD-affected pregnancy (adjusted odds ratio [OR] 5.2, 95% confidence interval [CI] 2.1-12.9). Women of other ethnic origins were not at increased associated risk compared with white women (women of Asian origin [n = 75 590]: adjusted OR 0.9, 95% CI 0.6-1.3; black women [n = 25 966]: adjusted OR 0.6, 95% CI 0.3-1.1; women of "other" ethnic origin [n = 10 009]: adjusted OR 0.1, 95% CI 0.02-0.9). INTERPRETATION: The associated risk of NTD-affected pregnancies was higher among women of First Nations origin than among women of other ethnic origins. The mechanisms for this discrepancy should be explored.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".