Operationalization of Frailty Using Eight Commonly Used Scales and Comparison of Their Ability to Predict All‐Cause Mortality
Bibliographic record
Abstract
OBJECTIVES: To operationalize frailty using eight scales and to compare their content validity, feasibility, prevalence estimates of frailty, and ability to predict all-cause mortality. DESIGN: Secondary analysis of the Survey of Health, Ageing and Retirement in Europe (SHARE). SETTING: Eleven European countries. PARTICIPANTS: Individuals aged 50 to 104 (mean age 65.3 ± 10.5, 54.8% female, N = 27,527). MEASUREMENTS: Frailty was operationalized using SHARE data based on the Groningen Frailty Indicator, the Tilburg Frailty Indicator, a 70-item Frailty Index (FI), a 44-item FI based on a Comprehensive Geriatric Assessment (FI-CGA), the Clinical Frailty Scale, frailty phenotype (weighted and unweighted versions), the Edmonton Frail Scale, and the FRAIL scale. RESULTS: All scales had fewer than 6% of cases with at least one missing item, except the SHARE-frailty phenotype (11.1%) and the SHARE-Tilburg (12.2%). In the SHARE-Groningen, SHARE-Tilburg, SHARE-frailty phenotype, and SHARE-FRAIL scales, death rates were 3 to 5 times as high in excluded cases as in included ones. Frailty prevalence estimates ranged from 6% (SHARE-FRAIL) to 44% (SHARE-Groningen). All scales categorized 2.4% of participants as frail. Of unweighted scales, the SHARE-FI and SHARE-Edmonton scales most accurately predicted mortality at 2 (SHARE-FI area under the receiver operating characteristic curve (AUC) = 0.77, 95% confidence interval (CI) = 0.75-0.79); SHARE-Edmonton AUC = 0.76, 95% CI = 0.74-0.79) and 5 (both AUC = 0.75, 95% CI = 0.74-0.77) years. The continuous score of the weighted SHARE-frailty phenotype (AUC = 0.77, 95% CI = 0.75-0.78) predicted 5-year mortality better than the unweighted SHARE-frailty phenotype (AUC = 0.70, 95% CI = 0.68-0.71), but the categorical score of the weighted SHARE-frailty phenotype did not (AUC = 0.70, 95% CI = 0.68-0.72). CONCLUSION: Substantive differences exist between scales in their content validity, feasibility, and ability to predict all-cause mortality. These frailty scales capture related but distinct groups. Weighting items in frailty scales can improve their predictive ability, but the trade-off between specificity, predictive power, and generalizability requires additional evaluation.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".