Predictive value of frailty in the mortality of hospitalized patients with COVID-19: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: The present study aimed to analyze the impact of frailty on mortality risk among hospitalized patients with coronavirus disease 2019 (COVID-19). Methods: Literature searches were conducted using the MEDLINE, Embase, and Cochrane databases for articles reporting the association between frailty and mortality in hospitalized patients with COVID-19. The quality of the included studies was assessed using the Newcastle-Ottawa scale (NOS). A random-effects meta-analysis was performed to calculate the pooled effects. Results: A total of 21 studies with 26,652 hospitalized patients were included. Sixteen studies used the Clinical Frailty Score (CFS), and five used other frailty assessment tools. The pooled estimates of frailty in hospitalized patients with COVID-19 were 51.4% [95% confidence interval (CI): 39.9–62.9%]. In the CFS group, frail patients experienced a higher rate of short-term mortality than non-frail patients [odds ratio (OR) =3.0; 95% CI: 2.3–3.9; I2=72.7%; P<0.001]. In the other tools group, frail patients had a significantly increased short-term mortality risk compared with non-frail patients (OR =2.4; 95% CI: 1.4–4.1; P=0.001). Overall, a higher short-term mortality risk was observed for frail patients than non-frail patients (OR =2.8; 95% CI: 2.3–3.5; P<0.001). In older adults, frail patients had a higher rate of short-term mortality than non-frail patients (OR =2.3; 95% CI: 1.8–2.9; P<0.001). Conclusions: Compared to non-frail hospitalized patients with COVID-19, frail patients suffered a higher risk of all-cause mortality, and this result was also found in the older adult group.
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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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.040 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".