Moving Frailty Toward Clinical Practice: NIA Intramural Frailty Science Symposium Summary
Bibliographic record
Abstract
Frailty has long been an important concept in the practice of geriatric medicine and in gerontological research, but integration and implementation of frailty concepts into clinical practice in the United States has been slow. The National Institute on Aging (NIA) Intramural Research Program and the Johns Hopkins Older Americans Independence Center sponsored a symposium to identify potential barriers that impede the movement of frailty into clinical practice and to highlight opportunities to facilitate the further integration of frailty into clinical practice. Primary and subspecialty care providers, and investigators working to integrate and translate new biological aging knowledge into more specific preventive and treatment strategies for frailty provided the meeting content. Recommendations included a call for more specific language that clarifies conceptual differences between frailty definitions and measurement tools; the development of randomized controlled trials to test whether specific intervention strategies for a variety of conditions differently affect frail and non-frail individuals; development of implementation studies and therapeutic trials aimed at tailoring care as a function of pragmatic frailty markers; the use of deep learning and dynamic systems approaches to improve the translatability of findings from epidemiological studies; and the incorporation of advances in aging biology, especially focused on mitochondria, stem cells, and senescent cells, toward the further development of biologically targeted intervention and prevention strategies that can be used to treat or prevent frailty. J Am Geriatr Soc 67:1559-1564, 2019.
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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 | 0.037 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.022 | 0.009 |
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".