30 THE DIAGNOSTIC AND PREDICTIVE ACCURACY OF THE EDMONTON FRAIL SCALE FOR SCREENING FRAILTY IN OLDER ADULTS: A SYSTEMATIC REVIEW AND META-ANALYSIS
Bibliographic record
Abstract
Abstract Background Frailty is a dynamic, multidimensional syndrome that is highly prevalent in older adults (65 years or older), which increases one’s dependency and vulnerability to adverse health outcomes, including mortality. The Edmonton Frail Scale (EFS) is a commonly used screening tool for identifying frailty in older adults. The purpose of this systematic review and meta-analysis is to determine the diagnostic test accuracy of the EFS to identify frailty in older adults, as well as the predictive accuracy of the EFS for adverse outcomes associated with frailty. Methods A literature search was performed across PubMed, EMBASE, CINAHL, MEDLINE and the Cochrane Library to identify all studies that validated the EFS across clinical settings. The methodological quality of the included studies was assessed using the QUADAS-2 tool. A bivariate random effects model generated pooled estimates of sensitivity and specificity. Results Eleven studies were included in the systematic review and six studies were included in meta-analysis. The diagnostic test accuracy of the EFS could not be proven from the included studies. The EFS was found to be more useful at ruling in rather than ruling out increased risk of mortality in individuals classified as frail, with a higher pooled specificity (0.75, 95% CI 0.64-0.84) than sensitivity (0.49, 95% CI 0.42-0.56). Conclusion The Edmonton Frail scale has limited ability as a frailty screening tool and should not be used in isolation to screen for frailty in older adults in clinical settings.
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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 | 0.034 | 0.086 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.059 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".