Reliability of the Frailty Index Among Community-Dwelling Older Adults
Bibliographic record
Abstract
BACKGROUND: Consistent and reproducible estimates of the underlying true level of frailty are essential for risk stratification and monitoring of health changes. The purpose of this study is to examine the reliability of the frailty index (FI). METHODS: A total of 426 community-dwelling older adults from the FRequent health Assessment In Later life (FRAIL70+) study in Austria were interviewed biweekly up to 7 times. Two versions of the FI, one with 49 deficits (baseline), and another with 44 (follow-up) were created. Internal consistency was assessed using confirmatory factor analysis and coefficient omega. Test-retest reliability was assessed with Pearson correlation coefficients and the intraclass correlation coefficient. Measurement error was assessed with the standard error of measurement, limits of agreement, and smallest detectable change. RESULTS: Participants (64.6% women) were on average 77.2 (±5.4) years old with mean FI49 at a baseline of 0.19 (±0.14). Internal consistency (coefficient omega) was 0.81. Correlations between biweekly FI44 assessments ranged between 0.86 and 0.94 and reliability (intraclass correlation coefficient) was 0.88. The standard error of measurement was 0.05, and the smallest detectable change and upper limits of agreement were 0.13; the latter is larger than previously reported minimal clinically meaningful changes. CONCLUSIONS: Both internal consistency and reliability of the FI were good, that is, the FI differentiates well between community-dwelling older adults, which is an important requirement for risk stratification for both group-level oriented research and patient-level clinical purposes. Measurement error, however, was large, suggesting that individual health deteriorations or improvements, cannot be reliably detected for FI changes smaller than 0.13.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".