Association of maternal risk factors with the recent rise of neural tube defects in Canada
Bibliographic record
Abstract
BACKGROUND: We sought to assess the recent trend in NTD prevalence at birth in the post-folic acid food fortification era and to identify the maternal risk factors associated with that trend. METHODS: We carried out a population-based study of all livebirths and stillbirths (including late pregnancy terminations) delivered in hospitals in Canada (excluding Quebec) from 2004 to 2015 (n = 3 439 330). We examined NTD birth prevalence by year, multiple pregnancy, maternal age, parity, pregestational diabetes, chronic illness, and problematic substance use. Poisson regression was used to quantify the association between spina bifida and cranial defects and maternal characteristics and other risk factors. RESULTS: = 0.03). Birth prevalence of spina bifida was higher among younger mothers, those with type 2 diabetes (rate ratio (RR) 3.74, 95% confidence interval (CI) 2.21, 6.35), chronic illness (RR 3.16, 95% CI 1.97, 5.07), and problematic substance use (RR 1.88, 95% CI 1.31, 2.71). Adjusting for risk factors attenuated the significant temporal trend in spina bifida (unadjusted average annual prevalence ratio (aAAPR) 1.016, 95% CI 1.001, 1.032; adjusted AAPR 1.014, 95% CI 0.998, 1.029). CONCLUSIONS: Increases in the frequency of maternal risk factors such as pregestational diabetes mellitus, substance use, and chronic illness may be partly responsible for the recent rise in NTDs, particularly spina bifida.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".