Urgent air transfers for acute respiratory infections among children from Northern Canada, 2005–2014
Bibliographic record
Abstract
BACKGROUND: The incidence of hospitalizations for acute respiratory infections (ARI) among young Indigenous children from Northern Canada is consistently high. ARIs requiring urgent air transfer can be life-threatening and costly. We aimed to describe their epidemiology, estimate age-specific incidences, and explore factors associated with level of care required. METHODS: We undertook a retrospective cohort study of children <5 years old from Northern Canada transferred by urgent air transport for ARI from 2005 through 2014 to 5 pediatric tertiary care centers in Vancouver, Edmonton, Winnipeg, Ottawa and Montreal. Admissions were identified via ARI-related ICD-9/10 coding and forward sortation area. Descriptive statistics and univariable analyses were performed. RESULTS: Among 650 urgent air transfers, the majority were from Nunavut (n = 349, 53.7%) or Nunavik (n = 166, 25.5%), <6 months old (n = 372, 57.2%), and without underlying comorbidity (n = 458; 70.5%). Estimated annual tertiary care ARI admission rates in infants <1 year old from Nunavut (40.7/1000) and Nunavik (44.5/1000) were tenfold higher than in children aged 1 to 4 years. Bronchiolitis (n = 333, 51.2%) and pneumonia (n = 208, 32.0%) were the most common primary discharge diagnoses. Nearly half required critical care (n = 316, 48.6%); mechanical ventilation rates ranged from 7.2% to 55.9% across centres. The most common primary pathogen was respiratory syncytial virus (n = 196, 30.1%). Influenza A or B was identified in 35 cases (5.4%) and vaccine-preventable bacterial infections in 27 (4.1%) cases. INTERPRETATION: Urgent air transfers for ARI from Northern Canada are associated with high acuity. Variations in levels of care were seen across referral centers, age groups and pathogens.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 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".