Prenatal opioid exposure and well-child care in the first 2 years of life: population-based cohort study
Bibliographic record
Abstract
OBJECTIVES: To quantify well-child visits by age 2 years and developmental screening at the 18-month enhanced well-child visit among children with prenatal opioid exposure (POE) and to identify factors associated with study outcomes. DESIGN: Population-based cohort study. SETTING: Ontario, Canada. PARTICIPANTS: 22 276 children with POE born 2014-2018 were classified as (1) 1-29 days of prescribed opioid analgesia, (2) 30+ days of prescribed opioid analgesia, (3) medication for opioid use disorder (MOUD), (4) MOUD and opioid analgesia, or (5) unregulated opioids. MAIN OUTCOME MEASURES: Attending ≥5 well-child visits by age 2 years and the 18-month enhanced well-child visit. Modified Poisson regression was used to examine factors associated with outcomes. RESULTS: Children with POE to 1-29 days of analgesics were most likely to attend ≥5 well-child visits (61.2%). Compared with these children, adjusted relative risks (aRRs) for ≥5 well-child visits were lower among those exposed to 30+ days of opioid analgesics (0.95, 95% CI 0.91 to 0.99), MOUD (0.83, 95% CI 0.79 to 0.88), MOUD and opioid analgesics (0.78 95% CI 0.68 to 0.90) and unregulated opioids (0.89, 95% CI 0.83 to 0.95). Relative to children with POE to 1-29 days of analgesics (58.5%), respective aRRs for the 18-month enhanced well-child visit were 0.92 (95% CI 0.88 to 0.96), 0.76 (95% CI 0.72 to 0.81), 0.76 (95% CI 0.66 to 0.87) and 0.82 (95% CI 0.76 to 0.88). Having a regular primary care provider was positively associated with study outcomes; socioeconomic disadvantage, rurality and maternal mental health were negatively associated. CONCLUSION: Well-child visits are low in children following POE, especially among offspring of mothers receiving MOUD or unregulated opioids. Strategies to improve attendance will be important for child outcomes.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".