ADVANCING GEROSCIENCE: NEW METHODS FOR GENOMIC EPIDEMIOLOGY OF AGING
Bibliographic record
Abstract
While chronological age is arguably the strongest risk factor for most major causes of death, and disease, same-aged individuals remain heterogeneous in their susceptibilities to these various outcomes. Quantifying the aging process, and in doing so, defining measurable estimates of ‘biological aging’ (in contrast to chronological aging) has become a major initiative in Geroscience research. In this symposium, leaders in the fields of epidemiology and biology of aging will discuss cutting-edge methods for quantifying the human aging process. These will include discussion of measures based on proteomic, genomic, and epigenomic data that are generated by applying multidisciplinary methods from systems biology, network analysis, and mathematical demography, and epidemiology. Dr. Ferrucci will describe a newly developed proteomic biomarker of aging; Dr. Sabastiani will describe protein signatures of aging and longevity in a centenarian cohort; Dr. Cohen, will discuss quantification of dysregulation in gene expression networks; and Dr. Levine will describe a novel and more informative epigenetic clock of aging and healthspan. Speakers will address the validity of these measures for predicting various aging outcomes, how they can be applied to capture epidemiological and demographic health trends, and describe potential underlying mechanism related to basic biology of aging. Finally, Dr. Belsky will discuss how genomic measures of aging can facilitate assessment of intervention efficacy, by providing a more immediate endpoint that can be measured at any stage in the lifescourse. He will also discuss the effect of precipitating social, behavioral, and demographic factors-providing further insight into epidemiologically observed health disparities.
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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 | 0.024 | 0.093 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".