Mortality in hospitalized older adults with COVID‐19 during three waves: A multicenter retrospective cohort study
Bibliographic record
Abstract
Background: The waves of COVID-19 infections in Ontario, Canada, were marked by differences in patient characteristics and treatment. Our objectives were to (i) describe patient characteristics, treatment, and outcomes of hospitalized older adults with COVID-19 between waves 1, 2, and 3, (ii) determine if there was an improvement in in-hospital mortality in waves 2 and 3 after adjusting for covariates. Methods: This retrospective cohort study was done in five acute care hospitals in Toronto, Ontario. Consecutive hospitalized older adults aged ≥65 years with confirmed COVID-19 infection were included. Wave 1 extended from March 11 to July 31, 2020, wave 2 from August 1, 2020 to February 20, 2021, and wave 3 from February 21 to June 30, 2021. Patient characteristics and outcomes were abstracted from charts. A logistic regression model was used to determine the association between COVID-19 and in-hospital mortality in waves 2 and 3 compared with wave 1. Results: Of the 1671 patients admitted to acute care, 297 (17.8%) were admitted in wave 1, 751 (44.9%) in wave 2, and 623 (37.3%) in wave 3. The median age of our cohort was 77.0 years (interquartile range: 71.0-85.0) and 775 (46.4%) were female. The prevalence of frailty declined in progressive waves. The use of dexamethasone, remdesivir, and tocilizumab was significantly higher in waves 2 and 3 compared with wave 1. In the unadjusted analysis, in-hospital mortality was unchanged between waves 1 and 2, but it was lower in wave 3 (18.3% vs. 27.4% in wave 1). After adjustment, in-hospital mortality was unchanged in waves 2 and 3 compared with wave 1. Conclusion: In-hospital mortality in hospitalized older adults with COVID-19 was similar between waves 1 and 3. Further research should be done to determine if COVID-19 therapies have similar benefits for older adults compared with younger adults.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".