Efficient and rapid assessment of multiple aspects of frailty using the Kyoto Frailty Scale, developed from the Edmonton Frail Scale
Bibliographic record
Abstract
[Purpose] Global aging has led to a dramatic increase in the number of frail people, who are likely to become bedridden. Since frailty can be partially reversed, early intervention would be beneficial for patients, family members, and clinicians. This study was designed to develop a screening tool for an accurate and comprehensive assessment of frailty by modulating the Edmonton Frail Scale (EFS). [Participants and Methods] The EFS, covering multiple domains, is one of the major diagnostic tools for frailty. Frail and non-frail participants (n=67) were evaluated for each diagnostic item of the EFS to identify the most efficient combination of questions by evaluating its sensitivity and specificity. [Results] The Kyoto Frailty Scale (KFS) was developed as a rapid frailty scale, based on the EFS. The KFS comprises nine questions about health status, polypharmacy, hospitalization, living with a reliable caregiver, shopping, transportation, housework, money management, and forgetting to take medicine. The KFS has an excellent negative predictive value (100%) for screening frailty and a positive predictive value (97%) for screening prefrailty and frailty if we regard KFS ≥4 as a test positive. [Conclusion] The KFS permits clinician to rapidly and accurately screen for frailty and prefrailty, or exclude 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".