Validation of Instruments to Classify the Frailty of the Elderly in Community
Bibliographic record
Abstract
Purpose: This study aimed to validate instruments to classify the frailty of Korean elderly people in community. Methods: For this study, 632 elders were selected from community-based elderly houses and home visiting registries, and data on frailty were collected using three instruments during November, 2008. The Korean Frail Scale (KFS) was composed of 10 domains with the maximum score of 20. The Edmonton Frail Scale (EFS) had 10 domains with the maximum score of 17. The 25_Japan Frail Scale (25_JFS) was composed of 6 domains with the maximum score of 25. Internal consistency was measured with Cronbach's ⍺. Sensitivity, specificity and area under the curve (AUC) of ROC were measured to see validity with long-term care insurance grade as a gold standard. Results: The Cronbach's ⍺ was .72 for KFS, .55 for EFS, and .80 for 25_JFS. Sensitivity, specificity, and AUC were 70.0%, 83.2%, and .83, respectively, at cutting point 10.5 for the KFS, 50.0%, 80.9%, and .66, respectively, at 8.5 for EFS, and 80.0%, 85.9%, and .86, respectively, at 12.5 for 25_JFS. Conclusion: KFS and three JFS showed favorable internal consistency and predictive validity. Further longitudinal studies are recommended to confirm predictive validity.
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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 | 0.028 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".