Prognostic accuracy of frailty & vulnerability screening in older ED patients: a systematic review & meta-analysis
Bibliographic record
Abstract
Abstract Frailty is associated with increased mortality and morbidity in older adults, highlighting the need for effective screening in emergency departments (EDs). However, the optimal tool for predicting adverse outcomes remains uncertain. Therefore, our review’s question is: which frailty and vulnerability screening instrument has the highest prognostic accuracy for adverse outcomes in older adults visiting EDs? Studies were eligible for inclusion if they included patients aged ≥60 admitted to the ED, used frailty or vulnerability instruments, and reported sensitivity, specificity, and area under the curve (AUC). Databases searched until January 25, 2025, included MEDLINE, EMBASE, Cochrane Library, CINAHL, and CNKI. Study quality was assessed using the updated Quality in Prognosis Studies (QUIPS) tool. Two investigators independently reviewed abstracts and full-texts. A meta-analysis was conducted for tools with consistent cutoffs in ≥ 4 studies. Sixty-seven papers described 62 studies using 40 screening instruments. The Identification of Seniors at Risk (ISAR) was the most frequently used (n = 25), followed by the Clinical Frailty Scale (CFS) (n = 15) and the Triage Risk Screening Tool (TRST)(n = 11). Most instruments showed high sensitivity but low specificity. Meta-analysis was feasible for ISAR and TRST. ISAR’s pooled sensitivity, specificity, and AUC for in-hospital mortality were 92% (95% CI, 88-95), 26% (95% CI, 21-33), and 0.82. For ED revisits, ISAR’s AUC was 0.64, while TRST’s was 0.60. Frailty screening instruments in EDs exhibit high sensitivity but low specificity, limiting clinical utility. Future research should enhance specificity by incorporating domains beyond geriatric conditions and known prognostic indicators into frailty instruments in large-scale studies.
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 | 0.016 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.041 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 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".