Editorial: Epidemiologic Reviews 2013--Special Issue on the Epidemiology of Aging
Bibliographic record
Abstract
The papers in this volume (1–13) reflect substantial and diverse epidemiologic research activity on some of the major health problems of older people. When the call for papers was announced, no particular themes within this vast field of the epidemiology of aging were suggested or encouraged. This led to a series of reviews covering a breadth of modern and important topics. These topics touch many disciplines that dovetail with the science of aging, including evaluating putative social and biological risk factors for age-related change and survivorship, identifying biological processes and modern biomarkers of aging, and exploring aging outcomes in older populations, such as the issue of multimorbidity among older Americans and military veterans in Canada and other countries. Several papers review risk factors for altered cognitive function and dementia-related illnesses. Although firm conclusions are not always possible given the state of the science, important directions for moving forward are offered. The preventive potential of these works is always close to the surface. The papers also encompass some of the general themes and challenges of population aging research. The importance of life-course concepts in the epidemiology of aging is present in several reports. For example, the report by Dahl and Hassing (2) finds an association of midlife obesity and late-life cognitive function. Other reports also address the problem of accurately identifying early life exposures that predict late-life age-related changes. The incorporation of modern biological indicators of aging and survivorship, such as genetic and genomic factors (13) and telomere length (9), reflects the importance of molecular approaches to the long-standing quest for biomarkers of the progression of age-related change, biomarkers that are precise and robust across populations. As these biomarkers are validated, they will take their places as useful prognostic factors for personalized medicine. However, to be useful, such biomarkers need to be generally independent of untoward environmental exposures and the variety of diseases that are prevalent in older populations, a common problem noted in the report by Salive (6). Other reports in this volume denote the great variation in health and function among older populations, highlighting the challenges for defining preventive approaches for older persons that can reach large segments of this very heterogeneous group. Although new methodological approaches are always welcome, collectively this collection of review papers bespeaks the active international activity in population aging and points to directions in understanding aging mechanisms; defining potential biological, clinical, and social intervention studies to enhance successful aging; and providing more targeted, efficient, and effective approaches to delivering health services to older persons. The epidemiology of aging is alive and well!
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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 | 0.012 | 0.039 |
| Meta-epidemiology (narrow) | 0.008 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.015 | 0.016 |
| Insufficient payload (model declined to judge) | 0.053 | 0.035 |
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".