Concurrent Validity of Pictorial Fit-Frail Scale (PFFS) in Older Adult Male Veterans with Different Levels of Health Literacy
Bibliographic record
Abstract
Introduction: Frailty is a state of vulnerability characterized by multisystemic physiological decline. The Pictorial Fit Frail Scale (PFFS) is a practical, image-based assessment that may facilitate the assessment of frailty in individuals with inadequate health literacy (HL). Objective: Determine the concurrent validity and feasibility of the PFFS in older Veterans with different levels of HL and cognition. Methods: Cross-sectional study in a geriatric clinic at a Veteran Health Administration (VHA) medical center. Veterans ≥65 years old completed a HL evaluation, PFFS, FRAIL scale and cognitive screening. We assessed the associations between PFFS, FRAIL scale, and VA-Frailty Index (VA-FI), and compared PFFS and FRAIL scale accuracy with a Receiver Operating Characteristic curve, Area Under the Curve (AUC) analysis, using the VA-FI as reference. Results: Eighty-three Veterans, mean age 76.20 ( SD = 6.02) years, 65.1% Caucasian, 69.9% had inadequate HL, 57.8% were frail and 20.5% had cognitive impairment. All participants completed the 43 PFFS items. There were positive correlations between PFFS and VA-FI, r = .55 (95% CI: 0.365–0.735, p < .001), and FRAIL scale, r = .673 (95% CI: 0.509–0.836, p < .001). Compared to the VA-FI, the PFFS (AUC = 0.737; 95% CI: 0.629–0.844) and FRAIL scale (AUC = 0.724;95% CI: 0.615–0.824; p < .001) showed satisfactory diagnostic accuracy. Conclusions: The PFFS is valid and feasible in evaluating frailty in older Veterans with different levels of HL and cognition.
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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 | 0.013 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".