Accuracy of functional tests to identify frail community elderly
Bibliographic record
Abstract
Objective: To verify the accuracy of functional tests in identifying frail older adults in two different regions. Methods: Observational, descriptive and cross-sectional study with the participation of 120 community older adults. Fried Phenotype and Edmonton Frail Scale were used to classify the frailty and the Timed Up and Go (TUG) and gait speed tests to identify the frail older adults. Results: In Ribeirão Preto and Lagarto, frail older adults performed TUG test in a longer time than pre-frail (p = 0.001) and non-frail (p < 0.001). As for gait speed, frail older adults had lower speed than non-frail (p = 0.01). The TUG test had moderate accuracy for the identification of frail older adults in Ribeirão Preto (AUC = 0.86, 95% CI 0.78 to 0.95, p < 0.001) and in Lagarto (AUC = 0.76, 95% CI 0.64 to 0.88, p = 0.001). Gait speed, on the other hand, is not accurate to discriminate frail older adults. The cut-off points for TUG with the highest sensitivity and specificity were 11.5 seconds for both older adults living in Ribeirão Preto and Lagarto. Conclusion: The TUG was capable of identifying frail older adults of two different regions, even when two different diagnostic methods of frailty were applied, standing out as a simple screening to be used in clinical practice.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".