Patient characteristics, clinical care, resource use, and outcomes associated with hospitalization for COVID-19 in the Toronto area
Bibliographic record
Abstract
Abstract Background Patient characteristics, clinical care, resource use, and outcomes associated with hospitalization for coronavirus disease (COVID-19) in Canada are not well described. Methods We described all adult discharges from inpatient medical services and medical-surgical intensive care units (ICU) between November 1, 2019 and June 30, 2020 at 7 hospitals in Toronto and Mississauga, Ontario. We compared patients hospitalized with COVID-19, influenza and all other conditions using multivariable regression models controlling for patient age, sex, comorbidity, and residence in long-term-care. Results There were 43,462 discharges in the study period, including 1,027 (3.0%) with COVID-19 and 783 (2.3%) with influenza. Patients with COVID-19 had similar age to patients with influenza and other conditions (median age 65 years vs. 68 years and 68 years, respectively, SD<0.1). Patients with COVID-19 were more likely to be male (59.1%) and 11.7% were long-term care residents. Patients younger than 50 years accounted for 21.2% of all admissions for COVID-19 and 24.0% of ICU admissions. Compared to influenza, patients with COVID-19 had significantly greater mortality (unadjusted 19.9% vs 6.1%, aRR: 3.47, 95%CI: 2.57, 4.67), ICU use (unadjusted 26.4% vs 18.0%, aRR 1.52, 95%CI: 1.27, 1.83) and hospital length-of-stay (unadjusted median 8.7 days vs 4.8 days, aRR: 1.40, 95%CI: 1.20, 1.64), and not significantly different 30-day readmission (unadjusted 8.6% vs 8.2%, aRR: 1.01, 95%CI: 0.72, 1.42). Interpretation Adults hospitalized with COVID-19 during the first wave of the pandemic used substantial hospital resources and suffered high mortality. COVID-19 was associated with significantly greater mortality, ICU use, and hospital length-of-stay than influenza.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".