ASSESSING FRAILTY USING THE FIT-FRAILTY APP IN A NONGERIATRIC PRACTICE: A FEASIBILITY STUDY
Bibliographic record
Abstract
Abstract Frailty is a common medical condition with a prevalence of 24% in adults ≥50 years when using the Frailty Index. Thus, assessing frailty is a priority. The Fit-Frailty Application (App) is a user-friendly and validated measure that incorporates disease-related, physical, cognitive, psychosocial, and functional aspects of frailty. The purpose of this study was to determine the feasibility of using the App in a non-geriatric clinic. We conducted a cross-sectional study in a rheumatology clinic in Hamilton, Ontario. We included participants ≥50 years with osteoporosis who understood English or attended with a caregiver. Our primary outcome was feasibility defined by recruitment rate (criteria for success 90%), length of time to complete the App by a non-healthcare professional (≤15 minutes), and safety/challenges of using the App. Our secondary outcome was to conduct an exploratory analysis between osteoporosis management (osteoporosis medication, vitamin D and calcium) and total Fit-Frailty score. Thirty participants were approached during a routine clinic visit and 25 agreed to participate (mean age 72.2±11.2; 88% female; 44% had higher education). The mean Fit-Frailty score was 0.24±0.14; scores ≥0.25 indicate frailty. Five chose not to participate citing other time commitments. The mean time to complete the App was 15.48±6.6 minutes with no adverse events. Challenges included the need for a private room and space to perform the gait assessment. We found no association between osteoporosis management and Fit-Frailty score (p>0.05). Despite not meeting our feasibility criterion for recruitment, the App was a feasible tool to measure frailty in a non-geriatric clinic.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".