BIOLOGICAL AGE: JUST A METAPHOR FOR HETEROGENEITY IN THE HEALTH OF PEOPLE AT THE SAME CHRONOLOGICAL AGE OR MORE?
Bibliographic record
Abstract
The concept of biological age (BA) as a measure of human health in individuals gains popularity. This concept is appealing to those who agree that the chronological age is a poor indicator of health status in individuals. The approaches to the estimation of BA, however, greatly diverge. Most estimation algorithms are based on the different types of regression models that relate the chronological age with a set of multiple biologically or clinically relevant traits – the markers of aging. Such algorithms span from “classical” least squares regression, to elastic nets and neuronal networks. The estimated values of BA depend on such algorithms, as even more on the biological/ clinical markers that are available. Unless we don’t understand why the different methods of the evaluation of BA are not closely agree with each other the BA would remain just a metaphor for the heterogeneity of people at the same chronological age.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.027 |
| Scholarly communication | 0.004 | 0.016 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".