AN OBJECTIVE MEASURE OF INDIVIDUAL HEALTH AND AGING FOR POPULATION SURVEYS
Bibliographic record
Abstract
To perform effective surveillance of aging populations and forecast trends in morbidity and mortality, simple, objective measures of individual health states are needed. We used biological principles to develop and validate a robust, flexible biomarker-based health index for use in population surveillance and economic analysis. Using 28 commonly available biomarkers in two datasets (WHAS and NHANES), we consider individuals as points in biomarker space and measure health as Mahalanobis distance to an “ideal state” of health, defined alternatively with population parameters or clinical thresholds. Surprisingly, population parameters outperform clinical thresholds, implying that clinical thresholds are imprecise and population means may provide a robust metric of healthy aging. Using the index, we show that low economic status is associated with accelerated biological aging in young (aged 20–40) and middle-aged (41–64), but not older adults (aged 65+) in our US datasets. We discuss implications for biological aging quantification in population surveillance.
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".