Measuring frailty in clinical practice: a comparison of physical frailty assessment methods in a geriatric out-patient clinic
Bibliographic record
Abstract
BACKGROUND: The objectives of this study were to determine: 1) the prevalence of frailty using Fried's phenotype method and the Short Performance Physical Battery (SPPB), 2) agreement between frailty assessment methods, 3) the feasibility of assessing frailty using Fried's phenotype method and the SPPB. METHODS: This cross-sectional study was conducted at a geriatric out-patient clinic in Hamilton, Canada. A research assistant conducted all frailty assessments. Patients were classified as non-frail, pre-frail or frail according to Fried's phenotype method and the SPPB. Agreement among methods is reported using the Cohen kappa statistic (standard error). Feasibility data included the percent of eligible participants agreeing to attempt the frailty assessments (criterion for feasibility: ≥90% of patients agreeing to the frailty assessment), equipment required, and safety considerations. A p-value of <0.05 is considered significant. RESULTS: A total of 110 participants (92%) and 109 participants (91%) agreed to attempt Fried's phenotype method and SPPB, respectively. No adverse events occurred during any assessments. According to Fried's phenotype method, the prevalence of frailty and pre-frailty was 35% and 56%, respectively, and according to the SPPB, the prevalence of frailty and pre-frailty was 50% and 35%, respectively. There was fair to moderate agreement between methods for determining which participants were frail (0.488 [0.082], p < 0.001) and pre-frail (0.272 [0.084], p = 0.002). CONCLUSIONS: Frailty and pre-frailty are common in this geriatric outpatient population, and there is fair to moderate agreement between Fried's phenotype method and the SPPB. Over 90% of the patients who were eligible for the study agreed to attempt the frailty assessments, demonstrating that according to our feasibility criteria, frailty can be assessed in this patient population. Assessing frailty may help clinicians identify high-risk patients and tailor interventions based on baseline frailty characteristics.
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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 | 0.035 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".