Trends in characteristics, interventions, and outcomes of hospitalized patients with COVID-19 in Canada: a multicentre prospective cohort study
Bibliographic record
Abstract
PURPOSE: Our objective was to investigate the temporal trends in baseline characteristics, interventions, and clinical outcomes in patients hospitalized with COVID-19 in Canada over five pandemic waves. METHODS: We conducted a multicentre prospective cohort study enrolling adults and children admitted with COVID-19 from 47 Canadian hospitals. We compared characteristics, interventions, and outcomes of patients across five distinct pandemic waves. RESULTS: We enrolled 5,285 patients between 2 January 2020 and 8 February 2022. The mean (standard deviation) age was 62.6 (21.0) yr; 41.2% (n = 2,176) were female, and 48% (n = 2,539) required admission to an intensive care unit (ICU), of whom 60.3% (n = 1,530) underwent invasive mechanical ventilation. The proportion of vaccinated patients increased over time. The proportion of vaccinated hospitalized patients progressing to require ICU admission fell over pandemic waves while the proportion of unvaccinated hospitalized patients progressing to require ICU admission did not. Patients were most commonly treated with corticosteroids (48.7%; n = 2,575); use of corticosteroids and other evidence-based treatments increased over time. Hospital mortality was 22.1% (n = 1,166) among all patients, 30.2% (n = 766) among those admitted to an ICU, and 37.9% (n = 580) among those requiring invasive mechanical ventilation. Younger age, absence of chronic cardiac or pulmonary disease, severity of illness at admission, and prior vaccination was associated with a lower mortality; however, pandemic wave itself was not. CONCLUSION: Among patients hospitalized in Canada with COVID-19, several clinical factors including prior vaccination were associated with lower mortality, but pandemic wave was not.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".