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Record W4403321519 · doi:10.1016/j.mad.2024.111997

Deciphering Osteosarcopenia through the hallmarks of aging

2024· review· en· W4403321519 on OpenAlexaff
Francisca Franulic, Felipe Salech, Daniel Rivas, Gustavo Duque

Bibliographic record

VenueMechanisms of Ageing and Development · 2024
Typereview
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsMcGill UniversityMcGill University Health Centre
Fundersnot available
KeywordsGerontologyMedicineAgeingSenescenceInternal medicine

Abstract

fetched live from OpenAlex

Osteosarcopenia is a major driver of functional loss and a risk factor for falls, fractures, disability and mortality in older adults, urgently requiring the development of effective interventions to address it. The hallmarks of aging provide a theoretical and practical framework that allows for the structured organization of current knowledge and the planning of new development lines. This article comprehensively reviews the currently available literature on the role of the Hallmarks of Aging in the development of osteosarcopenia, thereby offering a panoramic view of the state of the art and knowledge gaps in this field. • Osteosarcopenia is defined as the concomitant occurrence of osteopenia/osteoporosis and sarcopenia • Osteosarcopenia is associated to multiple adverse outcomes • Alterations in most of the Hallmarks of Aging are associated with muscle and bone loss • A geroscience approach could target several hallmarks of aging to simultaneously improve muscle and bone mass and function.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.905
Threshold uncertainty score0.692

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.090
GPT teacher head0.379
Teacher spread0.289 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations8
Published2024
Admission routes1
Has abstractyes

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