Postpartum maternal codeine therapy and the risk of adverse neonatal outcomes: A retrospective cohort study
Bibliographic record
Abstract
OBJECTIVES: To examine whether postpartum maternal prescription of codeine was associated with an increased risk of harm to newborns. DESIGN: Population-based retrospective cohort study. SETTING: Ontario, Canada, from April 1, 1998 to March 1, 2008. PARTICIPANTS: A total of 7804 mothers with publically-funded prescription drug coverage. Women who received a prescription for a codeine-containing product within 7 days following hospital discharge and their neonates were matched to 7804 mothers who did not receive codeine following delivery. MAIN OUTCOME MEASURES: The primary outcome was readmission of the neonate to hospital for any reason within 30 days. Secondary outcomes included arrival to hospital by ambulance, hospitalization for dehydration, for injury, any hospitalization involving resuscitation or assisted ventilation, and all-cause mortality. RESULTS: We studied 7804 infants whose mothers filled a prescription for codeine shortly after delivery and 7804 whose mothers did not. In the primary analysis, infants whose mothers received codeine were no more likely to be readmitted to hospital in the subsequent 30 days than children whose mothers did not (hazard ratio 0.95, 95% confidence interval (CI) 0.81-1.11). Moreover, we found no association between maternal codeine use and the other adverse neonatal outcomes studied. A stratified analysis revealed no differential risk among infants born by Caesarean section (hazard ratio 0.86; 95% CI 0.69-1.08). CONCLUSIONS: In this large population-based study, maternal prescription of codeine following delivery was not associated with death or hospitalization in the early neonatal period.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".