Are We Ill Because We Age?
Bibliographic record
Abstract
Growing elderly populations, sometimes referred to as gray (or silver) tsunami, are an increasingly serious health and socioeconomic concern for modern societies. Science has made tremendous progress in the understanding of aging itself, which has helped medicine to extend life expectancies. With the increase of the life expectancy, the incidence of chronic age-related diseases (ARDs) has also increased. A new approach trying to solve this problem is the concept of geroscience. This concept implies that the aging process itself is the common cause of all ARDs. The corollary and consequence of such thinking is that we can and should treat aging itself as a disease. How to translate this into the medical practice is a big challenge, but if we consider aging as a disease the problem is solved. However, as there is no common definition of what aging is, what its causes are, why it occurs, and what should be the target(s) for interventions, it is impossible to conclude that aging is a disease. On the contrary, aging should be strongly considered not to be a disease and as such should not be treated; nonetheless, aging is likely amenable to optimization of changes/adaptations at an individual level to achieve a better functional healthspan.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".