Validation of an index to estimate the prevalence of frailty among community-dwelling seniors.
Bibliographic record
Abstract
BACKGROUND: This study validates cut-points for a frailty index (FI) to identify seniors at risk of a hospital-related event and estimates the number of frail seniors living in the community. The FI developed by Rockwood and Mitnitski defines levels of frailty based on scores of 0 to 1.0. DATA AND METHODS: The cut-point validation was conducted using Stratum-Specific Likelihood Ratios applied to combined 2003 and 2005 Canadian Community Health Survey (CCHS) data, linked to hospital records from the Discharge Abstract Database (2002 to 2007). Based on the validated cut-points, frailty prevalence was estimated using 2009/2010 CCHS data. RESULTS: Seniors scoring more than 0.21 on the FI were considered to be at elevated risk of hospital-related events. Four additional frailty levels were identified: non-frail (0 to ≤0.1), pre-frail (>0.1 to ≤0.21), more frail (>0.30 to ≤0.35) (women only), and most frail (frail-group subset) (0.45 or more). The number of community-dwelling seniors considered to be frail was estimated at about 1 million (24%) in 2009/2010; another 1.4 million (32%) could be considered pre-frail. Frailty prevalence rose with age; was higher among women than among men; and varied by geographic location. INTERPRETATION: A cut-point of more than 0.21 can be used to identify frail seniors living in the community.
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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 | 0.019 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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 | 0.001 |
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".