The Pictorial Fit-Frail Scale: Developing a Visual Scale to Assess Frailty
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
BACKGROUND: Standardized frailty assessments are needed for early identification and treatment. We aimed to develop a frailty scale using visual images, the Pictorial Fit-Frail Scale (PFFS), and to examine its feasibility and content validity. METHODS: In Phase 1, a multidisciplinary team identified domains for measurement, operationalized impairment levels, and reviewed visual languages for the scale. In Phase 2, feedback was sought from health professionals and the general public. In Phase 3, 366 participants completed preliminary testing on the revised draft, including 162 UK paramedics, and rated the scale on feasibility and usability. In Phase 4, following translation into Malay, the final prototype was tested in 95 participants in Peninsular Malaysia and Borneo. RESULTS: The final scale incorporated 14 domains, each conceptualized with 3-6 response levels. All domains were rated as "understood well" by most participants (range 64-94%). Percentage agreement with positive statements regarding appearance, feasibility, and usefulness ranged from 66% to 95%. Overall feedback from health-care professionals supported its content validity. CONCLUSIONS: The PFFS is comprehensive, feasible, and appears generalizable across countries, and has face and content validity. Investigation into the reliability and predictive validity of the scale is currently underway.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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