Maternal and neonatal characteristics of a Canadian urban cohort receiving treatment for opioid use disorder during pregnancy
Bibliographic record
Abstract
The epidemic of prescription and non-prescription opioid misuse is of particular importance in pregnancy. The Society of Obstetricians and Gynaecologists of Canada currently recommends opioid replacement therapy with methadone or buprenorphine for opioid-dependent women during pregnancy. This vulnerable segment of the population has been shown to be at increased risk of blood-borne infectious diseases, nutritional insecurity and stress. The objective of this study was to describe an urban cohort of pregnant women on opioid replacement therapy and to evaluate potential effects on the fetus. A retrospective chart review of all women on opioid replacement therapy and their infants who delivered at The Ottawa Hospital General and Civic campuses between January 1, 2013 and March 24, 2017 was conducted. Data were collected on maternal characteristics, pregnancy outcomes, neonatal outcomes and corresponding placental pathology. Maternal comorbidities identified included high rates of infection, tobacco use and illicit substance use, as well as increased rates of placental abruption compared with national averages. Compared with national baseline averages, the mean neonatal birth weight was low, and the incidence of small for gestational age infants and congenital anomalies was high. The incidence of NAS was comparable with estimates from other studies of similar cohorts. Findings support existing literature that calls for a comprehensive interdisciplinary risk reduction approach including dietary, social, domestic, psychological and other supports to care for opioid-dependent women in 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".