The Prognostic Accuracy of Frailty and Vulnerability Screening for Older Adults in the Emergency Department: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
STUDY OBJECTIVE: Frailty and vulnerability are associated with increased morbidity and mortality in older adults, yet the optimal screening tool for predicting adverse outcomes in the emergency department (ED) remains unclear. Our question is: Which frailty or vulnerability screening instrument has the highest prognostic accuracy for adverse outcomes in older adults visiting the ED? METHODS: We included observational studies involving patients aged more than or equal to 60 years presenting to the ED that applied frailty or vulnerability instruments and reported sensitivity, specificity, and area under the curve (AUC). We searched MEDLINE, EMBASE, Cochrane Library, CINAHL, and CNKI through January 2025. Study quality was assessed using an updated the Quality in Prognosis Studies tool. Two investigators independently screened studies. Meta-analysis was conducted for instruments with consistent cutoffs reported in more than or equal to 4 studies. RESULTS: Fifty-seven studies (125,412 patients) assessed 39 instruments with varied cutoffs. The Identification of Seniors at Risk and Clinical Frailty Scale were studied most. Most tools demonstrated high sensitivity but low specificity. For 30-day mortality, pooled Identification of Seniors at Risk estimates were as follows: sensitivity 92% (95% confidence interval, 84 to 96), specificity 37% (26 to 50), likelihood ratio (LR)+ 1.47 (1.25 to 1.79), LR- 0.24 (0.11 to 0.42), and AUC 0.84 (0.60-0.89). Clinical Frailty Scale (cut-off of more than or equal to 5) showed sensitivity of 81% (62 to 91), specificity 71% (54 to 83), LR+ 2.80 (1.96 to 3.82), LR- 0.29 (0.15 to 0.48), and AUC 0.82 (0.77 to 0.85). CONCLUSION: Current screening instruments fail to identify older individuals who will experience adverse outcomes. Our findings suggest that EDs should reconsider relying on existing screening tools as standalone prognostic instruments and explore incorporating additional domains to improve accuracy.
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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 | 0.017 | 0.047 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.037 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".