Screening for frailty in primary care: Accuracy of gait speed and hand-grip strength.
Bibliographic record
Abstract
OBJECTIVE: To examine the accuracy of individual Fried frailty phenotype measures in identifying the Fried frailty phenotype in primary care. DESIGN: Retrospective chart review. SETTING: A community-based primary care practice in Kitchener, Ont. PARTICIPANTS: A total of 516 patients 75 years of age and older who underwent frailty screening. MAIN OUTCOME MEASURES: Using modified Fried frailty phenotype measures, frailty criteria included gait speed, hand-grip strength as measured by a dynamometer, and self-reported exhaustion, low physical activity, and unintended weight loss. Sensitivity, specificity, accuracy, and precision were calculated for single-trait and dual-trait markers. RESULTS: Complete frailty screening data were available for 383 patients. The overall prevalence of frailty based on the presence of 3 or more frailty criteria was 6.5%. The overall prevalence of individual Fried frailty phenotype markers ranged from 2.1% to 19.6%. The individual criteria all showed sensitivity and specificity of more than 80%, with the exception of weight loss (8.3% and 97.4%, respectively). The positive predictive value of the single-item criteria in predicting the Fried frailty phenotype ranged from 12.5% to 52.5%. When gait speed and hand-grip strength were combined as a dual measure, the positive predictive value increased to 87.5%. CONCLUSION: There is a need for frailty measures that are psychometrically sound and feasible to administer in primary care. While use of gait speed or grip strength alone was found to be sensitive and specific as a proxy for the Fried frailty phenotype, use of both measures together was found to be accurate, precise, specific, and more sensitive than other possible combinations. Assessing both measures is feasible within primary care.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".