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Record W2943513392 · doi:10.1111/jgs.15928

Moving Frailty Toward Clinical Practice: NIA Intramural Frailty Science Symposium Summary

2019· article· en· W2943513392 on OpenAlexaff
Jeremy Walston, Karen Bandeen‐Roche, Brian Buta, Howard Bergman, Thomas M. Gill, John E. Morley, Linda P. Fried, Thomas N. Robinson, Jonathan Afilalo, Anne B. Newman, Carlos López-Otı́n, Rafael de Cabo, Olga Theou, Stephanie A. Studenski, Harvey Jay Cohen, Luigi Ferrucci

Bibliographic record

VenueJournal of the American Geriatrics Society · 2019
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsDalhousie UniversityMcGill University
FundersNational Center for Advancing Translational SciencesNational Institute on AgingNational Institutes of Health
KeywordsMedicineSubspecialtyGerontologyGeriatricsIntervention (counseling)MEDLINERandomized controlled trialClinical trialSuccessful agingFamily medicineNursingPsychiatryPathology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.218
Threshold uncertainty score0.888

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.002
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.030
GPT teacher head0.359
Teacher spread0.329 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations179
Published2019
Admission routes1
Has abstractyes

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