Prenatal Substance Use Disorders and Dental Caries in Children
Bibliographic record
Abstract
Substance use is common in women of reproductive age, but limited data exist on the dental health of their children, including risk of caries. We conducted a longitudinal cohort study of 790,758 infants born between 2006 and 2016 in Quebec, Canada. We identified women with substance use disorders before or during pregnancy. The main outcome measure was hospitalization for dental caries in offspring up to 12 y after birth. We estimated hazard ratios (HRs) with 95% confidence intervals (CIs) for the association of maternal substance use with pediatric dental caries, adjusted for potential confounders. Children exposed to maternal substance use had a higher incidence of hospitalization for dental caries than unexposed children (105.2 vs. 27.0 per 10,000 person-years). Maternal substance use was associated with 1.96 times the risk of childhood dental caries (95% CI, 1.80-2.14), including a greater risk of caries of enamel, dentin, or cementum (HR, 2.00; 95% CI, 1.82-2.19) and dental pulp (HR, 2.36; 95% CI, 2.07-2.70), relative to no substance use. Associations were elevated for alcohol (HR, 2.31; 95% CI, 2.03-2.64) but were also present for cocaine, cannabis, opioids, and other substances. Substance use during pregnancy was more strongly associated with dental caries hospitalization than prepregnancy substance use. Associations were stronger in early childhood. Maternal substance use is associated with the future risk of dental caries hospitalization in children. Targeting substance use early in the lives of women may contribute to dental caries prevention in offspring.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| 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".