Association of a Modified Physiologic Index With Mortality and Incident Disability: The Health, Aging, and Body Composition Study
Bibliographic record
Abstract
BACKGROUND: Indexes constructed from components may identify individuals who age well across systems. We studied the associations of a Modified Physiologic Index (systolic blood pressure, forced vital capacity, Digit Symbol Substitution Test score, serum cystatin-C, serum fasting glucose) with mortality and incident disability. METHODS: Data are from the Health, Aging, and Body Composition study on 2,737 persons (51.2% women, 40.3% black) aged 70-79 years at baseline and followed on average 9.3 (2.9) years. Components were graded 0 (healthiest), 1 (middle), or 2 (unhealthiest) by tertile or clinical cutpoints and summed to calculate a continuous index score (range 0-10). We used multivariate Cox proportional hazards regression to calculate risk of death or disability and determined accuracy predicting death using the area under the curve. RESULTS: Mortality was 19% greater per index unit (p < .05). Those with highest index scores (scores 7-10) had 3.53-fold greater mortality than those with lowest scores (scores 0-2). The unadjusted index (c-statistic = 0.656, 95% CI 0.636-0.677, p < .0001) predicted death better than age (c-statistic = 0.591, 95% CI 0.568-0.613, p < .0001; for comparison, p < .0001). The index attenuated the age association with mortality by 33%. A model including age and the index did not predict death better than the index alone (c-statistic = 0.671). Prediction was improved with the addition of other markers of health (c-statistic = 0.710, 95% CI 0.689-0.730). The index was associated with incident disability (adjusted hazard ratio per index unit = 1.04, 95% CI 1.01-1.07). CONCLUSIONS: A simple index of available physiologic measurements was associated with mortality and incident disability and may prove useful for identifying persons who age well across systems.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".