One-Year Mortality and Hospital Readmission in Survivors of COVID-19 Critical Illness—A Population-Based Cohort Study
Bibliographic record
Abstract
OBJECTIVES: To evaluate 1-year outcomes (mortality, and recurrent hospital and ICU readmission) in adult survivors of COVID-19 critical illness compared with survivors of critical illness from non-COVID-19 pneumonia. DESIGN: Population-based retrospective observational cohort study. SETTING: Province of Ontario, Canada. PATIENTS: Six thousand ninety-eight consecutive adult patients (≥ 18 yr old) from 102 centers, admitted to ICU with COVID-19 (from January 1, 2020, to March 31, 2022), and surviving to hospital discharge. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The primary outcome was 1-year mortality. We also evaluated the number of emergency department (ED) visits, hospital readmissions, and ICU readmissions over this same time period. We compared patients using overlap propensity score-weighted, cause-specific proportional hazard models. Mean age was 59.6 years and 38.5% were female. Of these patients, 1610 (26.4%) and 375 (6.1%) were readmitted to hospital and ICU, respectively, and 917 (15.0%) died within 1 year. Compared with survivors of critical illness from non-COVID-19 pneumonia ( n = 2568), those who survived COVID-19 critical illness had a lower risk of ED visit (hazard ratio [HR], 0.65 [95% CI, 0.60-0.71]), hospital readmission (HR, 0.56 [95% CI, 0.51-0.62]), ICU readmission (HR, 0.44 [95% CI, 0.37-0.53]), and mortality (HR, 0.67 [95% CI, 0.58-0.78]) within 1 year. CONCLUSIONS: Risk of ED visit, hospital readmission, ICU readmission, and mortality within 1 year of discharge among survivors of COVID-19 critical illness was lower than survivors of critical illness from non-COVID-19 pneumonia.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".