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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".