Investigating the feasibility and reliability of the Pictorial Fit-Frail Scale
Bibliographic record
Abstract
BACKGROUND: the Pictorial Fit-Frail Scale (PFFS) was designed as a simple and practical approach to the identification of frailty. OBJECTIVES: To investigate the feasibility and reliability of this visual image-based tool, when used by patients, caregivers and healthcare professionals (HCPs) in clinical settings. DESIGN: observational study. SETTING: three outpatient geriatric healthcare settings. SUBJECTS: patients (n = 132), caregivers (n = 84), clinic nurses (n = 7) and physicians (n = 10). METHODS: the PFFS was administered to all patients. Where available, HCPs and caregivers completed the scale based on the patients' health. In the geriatric day hospital, the PFFS was completed on admission and administered again within 7-14 days. Time and level of assistance needed to complete the scale were recorded. Intraclass correlation coefficients (ICCs) and 95% confidence intervals (CIs) were used to assess test-retest and inter-rater reliability. RESULTS: mean time to complete the scale (minutes:seconds ± SD) was 4:30 ± 1:54 for patients, 3:13 ± 1:34 for caregivers, 1:28 ± 0:57 for nurses and 1:32 ± 1:40 for physicians. Most patients were able to complete the scale unassisted (64%). Mean patient PFFS score was 11.1 ± 5.3, mean caregiver score was 13.2 ± 6.3, mean nurse score was 10.7 ± 4.5 and mean physician score was 11.1 ± 5.6; caregiver scores were significantly higher than patient (P < 0.01), nurse (P < 0.001) and physician (P < 0.01) scores. Test-retest reliability was good for patients (ICC = 0.78, [95%CI = 0.67-0.86]) and nurses (ICC = 0.88 [0.80-0.93]). Inter-rater reliability between HCPs was also good (ICC = 0.75 [0.63-0.83]). CONCLUSION: the PFFS is a feasible and reliable tool for use with patients, caregivers and HCPs in clinical settings. Further research on the validity and responsiveness of the tool is necessary.
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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 | 0.016 | 0.046 |
| 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 | 0.001 | 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".