Paediatric health system impact of an early respiratory viral season in Eastern Ontario, Canada: A descriptive analysis
Bibliographic record
Abstract
Objectives: We examined trends in patient volumes and care intensity among children admitted with laboratory-confirmed respiratory viral infections over 5 years in Ottawa, following the most recent and intense respiratory viral season experienced throughout the Ontario paediatric health system. Methods: This was a retrospective cohort study of patients at the Children's Hospital of Eastern Ontario (CHEO) in Ottawa, who were diagnosed with a laboratory-confirmed respiratory viral infection in the first 72 h of admission between October 22, 2017 and March 25, 2023. Their admissions were stratified by age groups and level of care intensity, based on unit of admission and/or additional ventilatory needs, with Level 3 patients requiring intensive care unit admission, and evaluated for trends over six surveillance periods that began in Week 35 (early September) and ended in Week 34 (end-August) of the following year. Results: During the surveillance period from August 28, 2022 to March 25, 2023, there was an early, steep and twofold increase in admissions due to respiratory viral infections compared to previous periods, driven largely by Respiratory Syncytial Virus and Influenza A. Despite similar age distributions, there was a larger volume of Level 2 and 3 admissions, and higher proportion of patients requiring Level 2 care intensity in inpatient medicine units (23.4% versus 10.4% in pre-pandemic years; P < 0.001). Conclusions: The most recent viral season was associated with elevated volumes and higher inpatient acuity compared to previous years and underscores the need for additional operational and human health resources to support paediatric health systems through these predictable surge periods.
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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 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| 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".