Neonatal abstinence syndrome hospitalizations in Canada: a descriptive study
Bibliographic record
Abstract
OBJECTIVE: The objective of this paper is to describe the trend of newborn hospitalizations with neonatal abstinence syndrome (NAS) in Canada, between 2010 and 2020, and to examine severity indicators for these hospitalizations. METHODS: National hospitalization data (excluding Quebec) from the Canadian Institute for Health Information's Discharge Abstract Database, from January 2010 to March 2021, and Statistics Canada's Vital Statistics Birth Database were used. Analyses were performed to examine NAS hospitalizations by year and quarter, and by severity indicators of length of stay, Special Care Unit admission and status upon discharge. Severity indicators were further stratified by gestational age at birth. RESULTS: An increasing number and rate of NAS hospitalizations in Canada between 2010 (n = 1013, 3.5 per 1000 live births) and 2020 (n = 1755, 6.3 per 1000 live births) were identified. A seasonal pattern was observed, where rates of NAS were lowest from April to June and highest from October to March. Mean length of stay in acute inpatient care was approximately 15 days and 71% of NAS hospitalizations were admitted to the Special Care Unit. Hospitalizations for pre-term births with NAS had longer durations and greater rates of Special Care Unit admissions compared to term births with NAS. CONCLUSION: The number and rate of NAS hospitalizations in Canada increased during the study, and some infants required a significant amount of specialized healthcare. Additional research is required to determine what supports and education for pregnant people can reduce the incidence of NAS hospitalizations.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".