FRAILTY TRANSITIONS IN COMMUNITY DWELLING OLDER PEOPLE
Bibliographic record
Abstract
Background: Frailty is a dynamic process with transitions over time. Objectives: To examine frailty transitions and their relationship to health service utilization. Methods: Frailty status using the Vulnerable Elders Survey (VES-13) was determined for 608 community dwelling older people interviewed in a 2008 national survey and for 281 re-interviewed in 2014. The effect of frailty on death at 6 years was assessed using Cox proportional hazards analysis. Participants were divided into four groups based on their frailty transition. Demographic, functional and health characteristics were compared between the four groups using the Kruskal-Wallis and paired t-test. The independent association between the four frailty groups and health service utilization was assessed using logistic regression. Results: Between 2008 and 2014, 24% of 608 participants were lost to follow up, 9% were non frail, 37% were frail and 30% died. The Cox ratio showed that 86% of the non-frail in 2008 were alive six years later vs. 52% of the frail(HR 3.5 (CI 2.2–5.4)). Frailty transitions in the 281 participants interviewed at both time points revealed that 19% stayed non frail, 22% became frail, 22% stayed frail and 37% become more frail. Becoming frail, staying frail or becoming more frail compared to staying non frail was independently associated with a greater risk for requiring help on a regular basis, having a formal caregiver, and requiring home care.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".