Integrating Frailty Research into the Medical Specialties—Report from a U13 Conference
Bibliographic record
Abstract
Although the field of frailty research has expanded rapidly, it is still a nascent concept within the clinical specialties. Frailty, conceptualized as greater vulnerability to stressors because of significant depletion of physiological reserves, predicts poorer outcomes in several medical specialties, including cardiology, human immunodeficiency virus care, and nephrology, and in the behavioral and social sciences. Lack of a consensus definition, proliferation of measurement tools, inadequate understanding of the biology of frailty, and lack of validated clinical algorithms for frail individuals hinders incorporation of frailty assessment and frailty research into the specialties. In 2015, the American Geriatrics Society, the National Institute on Aging (NIA), and the Alliance for Academic Internal Medicine held a conference for awardees of the NIA-sponsored Grants for Early Medical/Surgical Specialists Transition into Aging Research program to review the current state of knowledge regarding frailty in the subspecialties and to highlight examples of integrating frailty research into the medical specialties. Research questions to advance frailty research into specialty medicine are proposed.
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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 | 0.064 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.014 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".