The Pictorial Fit-Frail Scale—Malay version (PFFS-M): reliability and validity testing in Malaysian primary care
Bibliographic record
Abstract
BACKGROUND: This study investigated the reliability and convergent validity of the PFFS-Malay version (PFFS-M) among patients (with varying educational levels), caregivers, and health care professionals (HCPs). PFFS-M cutoffs for frailty severity were developed. METHODS: This is a cross-sectional study from 4 primary care clinics where 240 patients aged >60 years and their caregivers were enrolled. Patients were assigned to a nurse or a health care assistant (HCA) for 2 separate PFFS-M assessments administered by HCPs of the same profession, as well as by a doctor during the first visit (inter-rater reliability). Patients were also administered the Self-Assessed Report of Personal Capacity & Healthy Ageing (SEARCH) tool, a 40-item frailty index, by a research officer. The correlation between patients' PFFS-M scores and SEARCH tool scores determined convergent validity. Patients returned 1 week later for PFFS-M reassessment by the same HCPs (test-retest reliability). Caregivers completed the PFFS-M for the patient at both clinic visits. Classification cut-points for the PFFS-M were derived against frailty categories defined through the SEARCH tool. RESULTS: The inter-rater (intraclass correlation coefficient [ICC] = 0.92 [95% CI, 0.90-0.93)] and test-retest (ICC = 0.94 [95% CI, 0.92-0.95]) reliability between all raters was excellent, including by patients' education levels. The convergent validity was moderate (r = 0.637, p < 0.001), including for varying educational background. PFFS-M categories were identified as: 0-3, no frailty; 4-5, at risk of frailty; 6-8, mild frailty; 9-12, moderate frailty; and >13, severe frailty. CONCLUSION: PFFS-M is a reliable and valid tool with frailty severity scores now established for use of this tool in primary care clinics.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".