CHALLENGES UNDERSTANDING AGING THROUGH BIOMARKERS ACROSS POPULATIONS: THE EXAMPLE OF CALCIUM
Bibliographic record
Abstract
Increasing availability of large clinical datasets including laboratory results raises the prospect of studying aging through these rich sources of biomarker data. However, substantial caution is warranted: most individual biomarkers fluctuate for many different reasons, and changes can have different interpretations in different contexts. This problem compounds with differences in population composition across data sets. Here, we illustrate this problem using the example of calcium. Using data from three cohort studies and sub-populations thereof, we show that calcium increases with age in some populations and decreases in others, and differs in its associations with mortality and frailty across populations. Different patterns emerge when considering calcium levels versus deviations from the normal values. Multivariate biomarker scores may partially mitigate these problems, but extreme caution is still warranted. More broadly, we should expect many findings about biomarkers and aging to be population-specific and should generalize only after empirical verification.
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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 | 0.094 | 0.217 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.007 | 0.016 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.006 | 0.015 |
| 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".