Retrospective Use of the Pictorial Fit-Frail Scale for Determination of Frailty Level in Hospitalized Older Adults with a Hip Fracture
Bibliographic record
Abstract
The Pictorial Fit-Frail Scale (PFFS) is a frailty tool consisting of visual images to comprehensively assess frailty across 14 domains that can be completed by health professionals, patients, or caregivers. The objective of this study was to explore the feasibility of using the PFFS retrospectively to determine a patient's frailty level using data from the hospital electronic health records (EHRs) of older adults admitted with an isolated hip fracture. A random sample of 200 hip fracture patients admitted to a Level 1 Trauma Center hospital in New Brunswick was selected for review using the PFFS. The majority (94.5%) of hospital EHRs contained the clinical information needed to populate most of the 14 PFFS domains, allowing for determination of a frailty score. The mean raw PFFS frailty score was 9.7 (SD 6.6), consistent with moderate frailty. For all patients, a Frailty Index (FI) score was calculated, with the mean being 0.27 (SD 0.18), again consistent with moderate frailty. Comparing the PFFS score to the FI score, the percentage categorized as not frail or very mildly frail fell from 33.3% to 20.1%, and those considered severely frail rose from 30.7% to 34.9%. The PFFS can be successfully used retrospectively with hospital EHRs to determine the frailty level of older patients. When converted to the FI score, there was an increase in the frequency and severity of frailty. This tool may provide a useful way to stratify older adults by frailty that can be helpful in evaluating health outcomes based on frailty levels.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".