Validation of the Pictorial Fit-Frail Scale in a Thoracic Surgery Clinic
Bibliographic record
Abstract
OBJECTIVE: Examine feasibility and construct validity of Pictorial Fit-Frail scale (PFFS) for the first time in older surgical patients. BACKGROUND: The PFFS uses visual images to measure health state in 14 domains and has been previously validated in outpatient geriatric clinics. METHODS: Patients ≥65 year-old who were evaluated in a multidisciplinary thoracic surgery clinic from November 2020 to May 2021 were prospectively included. Patients completed an in-person PFFS and Vulnerable Elders Survey (VES-13) during their visit, and a frailty index was calculated from the PFFS (PFFStrans). A geriatrician performed a comprehensive geriatric assessment (CGA) either in-person or virtually, from which a Frailty Index (FI-CGA) and Frailty Questionnaire (FRAIL) scale were obtained. To assess the validity of the PFFS in this population, the Spearman rank correlations (r spearman ) between PFFS trans and VES-13, FI-CGA, FRAIL were calculated. RESULTS: All 49 patients invited to participate agreed, of which 46/49 (94%) completed the PFFS so a score could be calculated. The majority of patients (59%) underwent an in-person CGA and the reminder (41%) a virtual CGA. The cohort was mainly female (59.0%), with a median age of 77 (range: 67-90). The median PFFS trans was 0.27 (interquartile range [IQR] 0.12-0.34), PFFS was 11 (IQR 5-14), and 0.24 (IQR 0.13-0.32) for FI-CGA. We observed a strong correlation between the PFFS trans and FI-CGA (r spearman = 0.81, P < 0.001) and a moderate correlation between PFFS trans and VES-13 and FRAIL score (r spearman = 0.68 and 0.64 respectively, P < 0.001). CONCLUSIONS: PFFS had good feasibility and construct validity among older surgical patients when compared to previously validated frailty measurements.
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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 | 0.006 | 0.019 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".