Development of an easy prognostic score for frailty outcomes in the aged
Bibliographic record
Abstract
BACKGROUND: identification of frailty is recommended in geriatric practice. However, there is a lack of frailty scores combining easy-to-collect predictors from multiple domains. OBJECTIVE: to develop a frailty score including only self-reported information and easy-to-perform standardised measurements recommended in routine geriatric practice. DESIGN: prospective population-based study. SETTING/PARTICIPANTS: included 1,007 Italian subjects aged 65 and over. MEASUREMENTS: seventeen baseline possible mortality predictors from several domains, 4-year risk of mortality and other adverse health outcomes associated with frailty [fractures, hospitalisation, and new and worsening activities of daily living (ADL) disability]. METHODS: a multivariate Cox model was used to identify the best sub-group of independent predictors and to develop a mortality prognostic score, defined as the number of adverse predictors present. Logistic regression was used to verify whether the score also predicted risk of other frailty outcomes in the cohort survivors. RESULTS: nine independent mortality predictors were identified. Among subjects with score > or =3, each one point increase in the score was associated with a doubling in mortality risk and, among survivors, with an increased risk of all the other adverse health outcomes. CONCLUSIONS: nine easy-to-collect predictors may identify aged people at increased risk of adverse health outcomes associated with frailty.
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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 | 0.000 |
| 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".