A Comparison of Two Approaches to Measuring Frailty in Elderly People
Bibliographic record
Abstract
BACKGROUND: Many definitions of frailty exist, but few have been directly compared. We compared the relationship between a definition of frailty based on a specific phenotype with one based on an index of deficit accumulation. METHODS: The data come from all 2305 people 70 years old and older who composed the clinical examination cohort of the second wave of the Canadian Study of Health and Aging. We tested convergent validity by correlating the measures with each other and with other health status measures, and analyzed cumulative index distributions in relation to phenotype. To test criterion validity, we evaluated survival (institutionalization and all-cause mortality) by frailty index (FI) score, stratified by the phenotypic definitions as "robust," "pre-frail," and "frail." RESULTS: The measures correlated moderately well with each other (R=0.65) and with measures of function (phenotypic definition R=0.66; FI R=0.73) but less well with cognition (phenotypic definition R=-0.35; FI R=-0.58). The median FI scores increased from 0.12 for the robust to 0.30 for the pre-frail and 0.44 for the frail. Survival was also lower with increasing frailty, and institutionalization was more common, but within each phenotypic class, there were marked differences in outcomes based on the FI values-e.g., among robust people, the median 5-year survival for those with lower FI values was 85%, compared with 55% for those with higher FI values. CONCLUSION: The phenotypic definition of frailty, which offers ready clinical operationalization, discriminates broad levels of risk. The FI requires additional clinical translation, but allows the risk of adverse outcomes to be defined more precisely.
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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 | 0.036 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".