Accuracy of the life-space mobility measure for discriminating frailty and sarcopenia in older people
Bibliographic record
Abstract
Abstract Objective To identify the profile of a sample of older people recruited at home based on a measure of life-space mobility and to establish the accuracy of the cut-off points of this instrument for discriminating between levels of frailty, frailty in walking speed and risk of sarcopenia. Method An observational methodological study of 391 participants aged ≥72 (80.4±4.6) years, who answered the Life-Space Assessment (LSA) and underwent frailty and risk of sarcopenia screening using the frailty phenotype and SARC-F measures, respectively, was performed. The cut-off points for frailty and risk of sarcopenia were determined using ROC (Receiver Operating Characteristic) curves and their respective 95% confidence intervals. Results Mean total LSA score was 53.6±21.8. The cut-off points with the best diagnostic accuracy for total LSA were ≤54 points for frailty in walking speed (AUC=0.645 95%; p<0.001) and ≤60 points for risk of sarcopenia (AUC=0.651 95%; p<0.001). Conclusion The ability of older people to move around life-space levels, as assessed by the LSA, proved a promising tool to screen for frailty in walking speed and risk of sarcopenia, thus contributing to the prevention of adverse outcomes.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.001 |
| 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".