Comparative Evaluation of Predictive Ability of Comprehensive Geriatric Assessment Components Including Frailty on Long-Term Mortality
Bibliographic record
Abstract
Background: This study aims to compare the predictive value of all comprehensive geriatric assessment (CGA) parameters with the predictive value of frailty assessment (with Edmonton Frailty Scale (EFS) and Fried Frailty Index (FFI)) for long-term mortality, in older adults.Methods: A total of 967 patients were included, consecutively. At the first admission, age, gender, comorbidities, number of drugs, and laboratory values of the patients were recorded. Each patient underwent CGA which consisted of anthropometric measurements, functional, cognitive, mood, nutritional, gait, fall, sleep duration, and frailty assessment. Fifty-seven months after the first admission, CGA parameters were analyzed to determine their predictive abilities on long-term mortality due to all causes, comparatively.Results: The median age was 73 years (range 65–94 years). The median follow-up time was 39.9 months (range 0.5–57.3 months). Serum albumin level, FFI, EFS, instrumental activity of daily living (IADL) score, and walking time were the best predictors of mortality. There was no significant difference between these parameters in predicting mortality.Conclusion: FFI and EFS have similar predictive value for mortality. In busy clinical practice, a new index based on IADL, walking time, and serum albumin level may be an alternative of frailty assessment for predicting mortality.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".