Advances in Geroscience: Impact on Healthspan and Chronic Disease
Bibliographic record
Abstract
Population aging is unprecedented, without parallel in human history, and the 21st century will witness even more rapid aging than did the century just past. Improvements in public health and medicine are having a profound effect on population demographics worldwide. By 2017, there will be more people over the age of 65 than under age 5, and by 2050, two billion of the estimated nine billion people on Earth will be older than 60 (http://unfpa.org/ageingreport/). Although we can reasonably expect to live longer today than past generations did, the age-related disease burden we will have to confront has not changed. With the proportion of older people among the global population being now higher than at any time in history and still expanding, maintaining health into old age (or healthspan) has become a new and urgent frontier for modern medicine. Geroscience is a cross-disciplinary field focused on understanding the relationships between the processes of aging and age-related chronic diseases. On October 30-31, 2013, the trans-National Institutes of Health GeroScience Interest Group hosted a Summit to promote collaborations between the aging and chronic disease research communities with the goal of developing innovative strategies to improve healthspan and reduce the burden of chronic disease.
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".