Geroscience: Aging and Oral Health Research
Bibliographic record
Abstract
Research in aging has significantly advanced; scientists are now able to identify interventions that slow the biologic aging processes (i.e., the "hallmarks of aging"), thus delaying the onset and progression of multiple diseases, including oral conditions. Presentations given during the 3-part session "Geroscience: Aging and Oral Health Research," held during the 2023 American Association for Dental, Oral, and Craniofacial Research meeting, are summarized in this publication. Speakers' topics spanned the translational research spectrum. Session 1 provided an overview of the geroscience and health span (disease-free and functional health throughout life) concepts. The common molecular mechanisms between oral cancer and aging were discussed, and research was presented that showed periodontal microflora as a potential factor in Alzheimer's disease progression. Session 2 focused on behavioral and social science aspects of aging and their oral health significance. The keynote provided evidence that loneliness and isolation can have major health effects. These social conditions, along with poor oral health, tooth loss, and cognitive decline, could potentially affect healthy eating ability and systemic health in older adults. Research could help elucidate the directions and pathways connecting these seemingly disparate conditions. Session 3 focused on the delivery of oral care in different settings and the many barriers to access care faced by older adults. Research is needed to identify and implement effective technology and strategies to improve access to dental care, including new delivery and financing mechanisms, workforce models, interprofessional provider education and practice, and use of big data from medical-dental integration of electronic health records. Research to improve the "oral health span," reduce oral health disparities, and increase health equity must be tackled at all levels from biologic pathways to social determinants of health and health policies.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.050 | 0.013 |
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".