Older adults with SARS‐CoV‐2 infection: Utility of the clinical frailty scale to predict mortality
Bibliographic record
Abstract
The objective of this study was to identify predictive factors of mortality in older adults with coronavirus disease 2019 (COVID-19), including the level of clinical frailty by using the clinical frailty scale (CFS). We analyzed medical records of all patients aged of 75 and older with a confirmed diagnosis of COVID-19 hospitalized in our Hospital between March 3 and April 25, 2020. Standardized variables were prospectively collected, and standardized care were provided to all patients. One hundred and eighty-six patients were included (mean 85.3 ± 5.78 year). The all cause 30-day mortality was 30% (56/186). At admission, dead patients were more dyspneic (57% vs. 38%, p = .014), had more often an oxygen saturation less than 94% (70% vs. 47%, p < .01) and had more often a heart rate faster than 90/min (70% vs. 42%, p < .001). Mortality increased in parallel with CFS score (p = .051) (20 deaths (36%) in 7-9 category). In multivariate analysis, CFS score (odds ratio [OR] = 1.49; confidence interval [CI] 95%, 1.01-2.19; p = .046), age (OR = 1.15; CI 95%, 1.01-1.31; p = .034), and dyspnea (OR = 5.37; CI 95%, 1.33-21.68; p = .018) were associated with all-cause 30-day mortality. It is necessary to integrate the assessment of frailty to determine care management plan of older patients with COVID-19, rather than the only restrictive criterion of age.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".