Hospital level of service, rural-urban location, and neonatal resuscitation interventions: A population study in Alberta Canada from 2000 to 2020
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Advanced neonatal resuscitation interventions (ANRIs) are rarely performed for late preterm and term infants. However, healthcare providers in community hospitals may need to perform ANRIs, while having limited experience and resources. Understanding practice differences between hospitals of different levels of service (LoS) and rural/urban location may inform quality improvement. Our objective are to a) examine how hospital LoS and rural/urban location relate to ANRI rates in Alberta, Canada, a public health system with standardized Neonatal Resuscitation Program® training and b) describe trends in neonatal resuscitation interventions and outcomes. METHODS/DESIGN: All live births ≥ 34 weeks in Alberta from 2000 to 2020 were examined using retrospective, administrative data. Hospitals (n = 97) were categorized based on availability of delivery support, cesarian sections, pediatricians/obstetricians, and NICUs, then subcategorized by population and proximity to metropolitan centres. Rates of individual interventions or any ANRI were compared. RESULTS: 966,475 births were included. ANRI rates were: intubation for ventilation (0.8%), chest compression (0.2%), epinephrine (0.02%), any ANRI (0.95%). While ANRIs were lower in community hospitals and home births, with lower hospital level of service, intubation rates decreased and chest compressions rates increased. Level 1A (OR:4.52, 95% CI 3.59-5.62) and home births (OR:3.09, 95% CI 2.52-3.76) had much higher odds of chest compressions. No pattern was observed between rural/remote sites of similar LoS. CONCLUSIONS: In this population study, there were higher chest compressions rates and lower intubation rates at hospitals without NICUs, despite standardized training. Reasons for this difference require further investigation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".