Neonatal opioid withdrawal and antenatal opioid prescribing
Bibliographic record
Abstract
BACKGROUND: The incidence of neonatal opioid withdrawal is increasing in both Canada and the United States. However, the degree to which the treatment of pain with opioids, rather than the misuse of prescription opioids or heroin, contributes to the prevalence of neonatal opioid withdrawal remains unknown. METHODS: We conducted a retrospective, population-based, cross-sectional study between 1992 and 2011 in Ontario with 2 objectives. First, we determined the annual incidence of neonatal abstinence syndrome. Second, using data from a subset of women eligible for publicly funded prescription drugs, we determined what proportion of women who deliver an infant with neonatal abstinence syndrome were given a prescription for an opioid before and during pregnancy. RESULTS: The incidence of neonatal abstinence syndrome in Ontario increased 15-fold during the study period, from 0.28 per 1000 live births in 1992 to 4.29 per 1000 live births in 2011. During the final 5 years of the study, we identified 927 deliveries of infants with neonatal abstinence syndrome to mothers who were public drug plan beneficiaries. Of these mothers, 67% had received an opioid prescription in the 100 days preceding delivery, including 53.3% who received methadone, an increase from 28.6% in the interval spanning 1 to 2 years before delivery (p < 0.001). Prescription for nonmethadone opioids decreased from 38% to 17% (p < 0.001). INTERPRETATION: The incidence of neonatal opioid withdrawal in Ontario has increased substantially over the last 20 years. Most of the women in this cohort who delivered an infant with neonatal abstinence syndrome had received a prescription for an opioid both before and during their pregnancy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".