New Persistent Opioid Use After Childbirth
Bibliographic record
Abstract
OBJECTIVE: To examine factors associated with new persistent opioid use after childbirth. METHODS: We conducted a population-based cohort study of individuals who initiated opioid therapy within 7 days of discharge from hospital after delivery between September 1, 2013, and September 30, 2021. The primary outcome was new persistent opioid use , which was defined as one or more prescriptions for an opioid within 90 days of the first postpartum prescription and one or more subsequent opioid prescriptions in the 91-365 days afterward. We used multivariable logistic regression to assess patient-, pregnancy-, and prescription-related factors associated with new persistent opioid use after delivery. RESULTS: We identified 118,694 unique deliveries after which opioids were initiated, including 99,399 cesarean (83.7%) and 19,295 vaginal (16.3%) deliveries. Among mothers who initiated an opioid after delivery, 1,282 (10.8/1,000 deliveries) met our definition of new persistent opioid use in the subsequent year. Rates of new persistent opioid use were appreciably higher after vaginal (16.0/1,000) compared with cesarean (9.8/1,000) deliveries. Each additional 30 morphine milligram equivalents in the initial opioid prescription was associated with an increased risk of new persistent use after cesarean (adjusted odds ratio [aOR] 1.06, 95% CI 1.04-1.08) and vaginal (aOR 1.05, 95% CI 1.02-1.08) delivery. A concomitant benzodiazepine prescription after cesarean delivery was associated with a markedly increased risk of persistent opioid use (aOR 2.69, 95% CI 1.60-4.52). CONCLUSION: Among people who filled an opioid prescription after delivery, about 1% displayed evidence of persistent opioid use in the subsequent year. Initial prescriptions for large quantities of opioids and a concurrent benzodiazepine prescription may be important modifiable risk factors to prevent new persistent opioid use after delivery.
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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 | 0.004 |
| 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".