The burden of COVID-19 care in community and academic intensive care units in Ontario, Canada: a retrospective cohort study
Bibliographic record
Abstract
PURPOSE: During the COVID-19 pandemic, neighbourhoods with high material deprivation and high proportions of racialized Canadians were disproportionately affected by COVID-19. Many of these neighbourhoods were served by community hospitals. We sought to compare the burden of COVID-19 care in community and academic intensive care units (ICUs) in Ontario, Canada. METHODS: We included all adult patients admitted to Ontario ICUs with COVID-19 between 1 March 2020 and 31 July 2021 in a retrospective cohort study. We compared patient volumes, demographics, interventions, and outcomes between community hospital corporations (CHCs) and academic hospital corporations (AHCs). RESULTS: During the first three waves of the pandemic, 9,651 adult ICU admissions for COVID-19 were reported across 72 hospital corporations in Ontario: 6,902 (71.5%) in CHCs and 2,749 (28.5%) in AHCs. Days of ICU care per baseline ICU bed were highest in large CHCs (> 10 baseline ICU beds) relative to AHCs and small CHCs (median [interquartile range], 73.7 [53.8-110.6] vs 42.2 [32.7-71.8] vs 21.4 [7.2-40.3]; Kruskal-Wallis test, P < 0.001). Among direct ICU admissions, CHC patients had greater severity of illness whereas among transfer ICU admissions, AHC patients were more severely ill. In a multivariable logistic regression model, mortality was similar among patients with index admission to a CHC or AHC; however, patients with index admission to an AHC were more likely to receive extracorporeal membrane oxygenation (adjusted odds ratio, 6.16; 95% confidence interval, 4.72 to 8.11). CONCLUSION: During the pandemic, Ontario's large CHCs provided significantly more days of ICU COVID-19 care per baseline ICU bed compared with AHCs and small CHCs. Equipping large CHCs to handle ICU surges during future emerging disease outbreaks should be a priority for pandemic preparedness.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".