Defining terms commonly used in sarcopenia research: a glossary proposed by the Global Leadership in Sarcopenia (GLIS) Steering Committee
Bibliographic record
Abstract
METHODS: The aim of this paper is to define terms commonly related to sarcopenia to enable standardization of these terms in research and clinical settings. The Global Leadership Initiative in Sarcopenia (GLIS) aims to bring together leading investigators in sarcopenia research to develop a single definition that can be utilized worldwide; work on a global definition of sarcopenia is ongoing. The first step of GLIS is to develop the common terminology, or a glossary, that will facilitate agreement on a global definition of sarcopenia as well as interpretation of clinical and research findings. RESULTS: Several terms that are commonly used in sarcopenia research are defined, including self-reported measures of function and ability; objective physical performance tests; and measures related to muscle function and size. CONCLUSION: As new methods and technologies are developed, these definitions may be expanded or refined over time. Our goal is to promote this common language to describe sarcopenia and its components in clinical and research settings in order to increase clinical awareness and research interest in this important condition. We hope that the use of common terminology in sarcopenia research will increase understanding of the concept and improve communication around this important age-related condition.
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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 | 0.091 | 0.122 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.028 | 0.019 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.007 | 0.018 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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".