A longitudinal analysis of temporal and spatial incidence of neonatal abstinence syndrome in Ontario: 2003-2016
Bibliographic record
Abstract
OBJECTIVE: This study describes the incidence of neonatal abstinence syndrome (NAS) in Ontario, Canada by year and health region from 2003 to 2016. DESIGN: The incidence of NAS diagnoses per 1,000 live births was calculated for the 36 local public health agency regions in Ontario from 2003 to 2016 using retrospective hospital admissions data. Infants with a diagnosis of NAS were identified using ICD-10 code P961. Local public health agency level data were aggregated and analyzed by geographic region and by Statistics Canada 2015 Peer Groups. RESULTS: The incidence of NAS in Ontario increased from 0.99 per 1,000 live births in 2003 to 5.94 per 1,000 live births in 2016. There were major differences in NAS incidence by geography, North Western Ontario had the greatest incidence across all years. Health regions with a rural and population center mix or mostly rural population had greater incidence rate of NAS compared to health regions with high density population centers. CONCLUSIONS: The incidence of NAS has dramatically increased across Ontario in the last decade. Actions should be taken to combat the continued increase in NAS rates, especially in health regions with disproportionately high incidence of NAS.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".