DETERMINATION OF CLINICALLY MEANINGFUL CHANGES IN FRAILTY MEASURES
Bibliographic record
Abstract
This research is aimed to determine the clinically meaningful change of self-reported and performance-based frailty measures. We analyzed data from a prospective cohort study of 1,084 community-dwelling older adults (mean age: 75.9 years; male 48.0%) who underwent assessments of frailty and health-related quality of life using the EuroQol-5D (range: 0–1) at baseline and 1 year later. Frailty measures included a deficit-accumulation frailty index (FI) (29 self-reported items alone or with additional 5 performance items), the frailty phenotype, the FRAIL questionnaire, and the Study of Osteoporotic Fracture (SOF) index. A clinically meaningful change was determined for each frailty measure that corresponded to a small decline (0.1–0.2) or large decline (>0.2) in the EuroQol-5D score over 1 year. After excluding people who died (n=16), were institutionalized (n=78), and were lost to follow-up (n=122), repeat frailty measures were available in 868 participants (80.1%). Using the change in EuroQol-5D score as the anchor, small and large clinically meaningful changes of frailty measures were 0.032 and 0.070 for the self-reported FI; 0.028 and 0.059 for the full FI; 0.076 and 0.588 for the frailty phenotype; 0.241 and 0.379 for the FRAIL questionnaire; and 0.034 and 0.293 for the SOF index. Based on responsiveness indices, per-group sample sizes to achieve 80% power in clinical trials ranged from 66 (self-reported FI) to 4941 (SOF index) for small change and 15 (self-reported FI) to 126 (FRAIL questionnaire) for large change. These results can inform the choice of frailty measures in interventional studies targeting frailty.
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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 | 0.012 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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".