The Pictorial Fit-Frail Scale: Developing a Visual Scale to Assess Frailty
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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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".