Efficient and rapid assessment of multiple aspects of frailty using the Kyoto Frailty Scale, developed from the Edmonton Frail Scale
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 it