Maternal Substance Abuse and the Later Risk of Fractures in Offspring: L’abus maternel de substances et le risque ultérieur de fractures chez les enfants
Bibliographic record
Abstract
OBJECTIVE: To assess the association of maternal illicit drug abuse before or during pregnancy with future fractures in offspring. METHODS: We performed a longitudinal cohort study of 792,022 infants born in hospitals of Quebec, Canada, between 2006 and 2016, with 5,457,634 person-years of follow-up. The main exposure was maternal substance abuse before or during pregnancy, including cocaine, opioid, cannabis, and other illicit drugs. The main outcome measure was hospitalization for traumatic fracture in offspring up to 12 years of age. We used adjusted Cox regression models to compute hazard ratios (HR) and 95% confidence intervals (CI) for the association of maternal drug abuse with the subsequent risk of fracture in children. RESULTS: The incidence of child fractures was higher for maternal illicit drug abuse than no drug abuse (21.2 vs. 15.4 per 10,000 person-years). Maternal drug abuse before or during pregnancy was associated with 2.35 times the risk of assault-related fractures (95% CI, 1.29 to 4.27) and 2.21 times the risk of transport accident-related fractures (95% CI, 1.34 to 3.66), compared with no drug abuse. Associations were strongest before 6 months of age for assault-related fractures (HR = 2.14; 95% CI, 0.97 to 4.72) and after 6 years for transport-related fractures (HR = 2.86; 95% CI, 1.35 to 6.05). Compared with no drug abuse, associations with assault and transport-related fractures were elevated for all drugs including cocaine, opioids, and cannabis. CONCLUSIONS: Maternal illicit drug abuse is associated with future child fractures due to assault and transport accidents.
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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 | 0.000 | 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".