Impact of Prenatal Exposure to Opioids, Cocaine, and Cannabis on Eye Disorders in Children
Bibliographic record
Abstract
OBJECTIVES: Prenatal substance exposure is associated with abnormal visual evoked potentials in offspring, but whether ocular abnormalities are present past infancy is unclear. We determined the association between prenatal substance exposure and hospitalizations for eye disorders in childhood. METHODS: We conducted a longitudinal cohort study of 794,099 infants born between 2006 and 2016 in all hospital centers in Quebec, Canada. We identified infants prenatally exposed to opioids, cocaine, cannabis, and other illicit substances and followed them over time to assess eye disorders that required in-hospital treatment, including retinal detachment and breaks, strabismus, and other ocular pathologies. We calculated incidence rates and hazard ratios (HR) with 95% confidence intervals (CI) for the association of prenatal substance exposure with risk of eye disorders, adjusted for patient characteristics. RESULTS: Infants exposed to substances prenatally had a higher incidence of hospitalizations for eye disorders compared with unexposed infants (47.0 vs 32.0 per 10,000 person-years). Prenatal substance exposure was associated with 1.23 times the risk of hospital admission for any eye disorder during childhood compared with no exposure (95% CI 1.04-1.45). Risks were greatest for strabismus (HR 1.55, 95% CI 1.16-2.07) and binocular movement disorders (HR 1.96, 95% CI 1.00-3.83). Opioid use was strongly associated with the risk of ocular muscle disorders (HR 3.15, 95% CI 1.98-5.01). CONCLUSIONS: Prenatal substance exposure is significantly associated with future hospitalizations for eye disorders in childhood. Efforts to minimize substance use in women of reproductive age are needed in light of the current opioid epidemic.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".