Clinical Correlates and Implications of the Reliability of the Frailty Index in the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
BACKGROUND: Deficit accumulation frailty indices (FIs) are widely used to characterize frailty. FIs vary in number and composition of items; the impact of this variation on reliability and clinical applicability is unknown. METHOD: We simulated 12 000 studies using a set of 70 candidate deficits in 12 080 community-dwelling participants 65 years and older. For each study, we varied the number (5, 10, 15, 25, 35, 45) and composition (random selection) of items defining the FI and calculated descriptive and predictive estimates: frailty score, prevalence, frailty cutoff, mortality odds ratio, predicted probability of mortality for FI = 0.28 (prevalence threshold), and FI cutoff predicting 10% mortality over the follow-up. We summarized the estimates' medians and spreads (0.025-0.975 quantiles) by number of items and calculated intraclass correlation coefficients (ICCs). RESULTS: Medians of frailty scores were 0.11-0.12 with decreasing spreads from 0.04-0.24 to 0.10-0.14 for 5-item and 45-item FIs. The median cutoffs identifying 15% as frail was 0.19-0.20 and stable; the spreads decreased with more items. However, medians and spreads for the prevalence of frailty (median: 11%-3%), mortality odds ratio (median: 1.24-2.19), predicted probability of mortality (median: 8%-17%), and FI cutoff predicting 10% mortality (median: 0.38-0.20) varied markedly. ICC increased from 0.19 (5-item FIs) to 0.84 (45-item FIs). CONCLUSIONS: Variability in the number and composition of items of individual FIs strongly influences their reliability. Estimates using FIs may not be sufficiently stable for generalizing results or direct application. We propose avenues to improve the development, reporting, and interpretation of FIs.
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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 | 0.068 | 0.226 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".