Validation of a Claims-Based Frailty Index Against Physical Performance and Adverse Health Outcomes in the Health and Retirement Study
Bibliographic record
Abstract
BACKGROUND: A claims-based frailty index (CFI) was developed based on a deficit-accumulation approach using self-reported health information. This study aimed to independently validate the CFI against physical performance and adverse health outcomes. METHODS: This retrospective cohort study included 3,642 community-dwelling older adults who had at least 1 health care encounter in the year prior to assessments of physical performance in the 2008 Health and Retirement Study wave. A CFI was estimated from Medicare claims data in the past year. Gait speed, grip strength, and the 2-year risk of death, institutionalization, disability, hospitalization, and prolonged (>30 days) skilled nursing facility (SNF) stay were evaluated for CFI categories (robust: <0.15, prefrail: 0.15-0.24, mildly frail: 0.25-0.34, moderate-to-severely frail: ≥0.35). RESULTS: The prevalence of robust, prefrail, mildly frail, and moderate-to-severely frail state was 52.7%, 38.0%, 7.1%, and 2.2%, respectively. Individuals with higher CFI had lower mean gait speed (moderate-to-severely frail vs robust: 0.39 vs 0.78 m/s) and weaker grip strength (19.8 vs 28.5 kg). Higher CFI was associated with death (moderate-to-severely frail vs robust: 46% vs 7%), institutionalization (21% vs 5%), activity of daily living disability (33% vs 9%), instrumental activity of daily living disability (100% vs 22%), hospitalization (79% vs 23%), and prolonged SNF stay (17% vs 2%). The odds ratios per 1-SD (=0.07) difference in CFI were 1.46-2.06 for these outcomes, which remained statistically significant after adjustment for age, sex, and a comorbidity index. CONCLUSION: The CFI is useful to identify individuals with poor physical function and at greater risks of adverse health outcomes in Medicare data.
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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 | 0.015 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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 | 0.000 |
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".