Neonatal Abstinence Syndrome and Associated Neonatal and Maternal Mortality and Morbidity
Bibliographic record
Abstract
OBJECTIVES: We examined demographic characteristics and birth outcomes of infants with neonatal abstinence syndrome (NAS) and their mothers in Canada. METHODS: = 2 881 789). Demographic characteristics, NAS, and neonatal and maternal morbidities were identified from delivery hospitalization data (including diagnostic codes). The main composite outcomes were maternal and neonatal mortality and/or severe morbidity, including death and potentially life-threatening conditions in the mother and the infant, respectively. Logistic regression yielded adjusted odds ratios (aORs) and 95% confidence intervals (CIs). RESULTS: The study included 10 027 mother-infant dyads with NAS. The incidence of NAS increased from 0.20% to 0.51%. Maternal mortality was 1.99 vs 0.31 per 10 000 women in the NAS group versus the comparison group (aOR = 6.53; 95% CI: 1.59 to 26.74), and maternal mortality and/or severe morbidity rates were 3.10% vs 1.35% (aOR = 2.21; 95% CI: 1.97 to 2.49). Neonatal mortality was 0.12% vs 0.19% (aOR = 0.28; 95% CI: 0.15 to 0.53), and neonatal mortality and/or severe morbidity rates were 6.36% vs 1.73% (aOR = 2.27; 95% CI: 2.06 to 2.50) among infants with NAS versus without NAS. CONCLUSIONS: NAS incidence increased notably in Canada between 2005-2006 and 2015-2016. Infants with NAS had elevated severe morbidity, and their mothers had elevated mortality and severe morbidity. These results highlight the importance of implementing integrated care services to support the mother-infant dyad during childbirth and in the postpartum 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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".