Association between mothers’ postoperative opioid prescriptions and opioid-related events in their children: A population-based cohort study
Bibliographic record
Abstract
BACKGROUND: Postoperative opioid prescriptions may be associated with risks of unintentional poisoning and drug diversion in other household members. The objective of this study was to explore the association between mothers' postoperative opioid prescriptions and incidence of opioid-related events in their children (aged 1 to 24 years). DATA AND METHODS: This retrospective cohort study used individually linked administrative health data from Ontario, Canada. A population-based sample of 170,156 opioid-naïve mothers (aged 15 to 64) (see Figure 1) who underwent surgery between 2013 and 2017 in Ontario was linked through birth records to create a cohort of their 283,550 opioid-naïve children (aged 1 to 24). The association between postoperative opioid analgesic prescriptions filled by mothers within seven days of discharge after surgery and opioid-related events (emergency department presentations or inpatient admissions for opioid poisoning, or mental and behavioural disorders attributable to opioid use) in their children within one year of their mother's discharge was assessed. RESULTS: Overall, 60.4% of the children in the cohort had a mother who filled a postoperative opioid prescription. The incidence of opioid-related events in children in the year after a mother's surgery was low overall (n=36/283,550, 0.01%), but higher among children whose mother filled a postoperative opioid prescription (n=29/171,139, 0.02%, vs. n=7/112,411, 0.01%, p=0.02), including in an analysis adjusting for child's age, mother's age, rural residence, neighbourhood income quintile and mother's Charlson comorbidity index score (adjusted odds ratio, 2.42 [95% confidence interval (CI), 1.05 to 5.54], p=0.04). DISCUSSION: Postoperative opioid prescriptions for mothers may contribute to opioid-related events in their children. These findings further underscore the importance of safe, effective opioid prescribing, as well as of patient and public education about the use, storage and disposal of these medications.
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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 | 0.000 | 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".