Health outcomes of sarcopenia: a consensus report by the outcome working group of the Global Leadership Initiative in Sarcopenia (GLIS)
Bibliographic record
Abstract
The Global Leadership Initiative in Sarcopenia (GLIS) aims to standardize the definition and diagnostic criteria for sarcopenia into one unifying, common classification. Among other actions to achieve this objective, the GLIS has organized three different working groups (WGs), with the WG on outcomes of sarcopenia focusing on reporting its health outcomes to be measured in clinical practice once a diagnosis has been established. This includes sarcopenia definitions that better predict health outcomes, the preferred tools for measuring these outcomes, and the cutoffs defining normal and abnormal values. The present article synthesizes discussions and conclusions from this WG, composed of 13 key opinion leaders from different continents worldwide. Results rely on systematic reviews, meta-analyses, and relevant cohort studies in the field. With a high level of evidence, sarcopenia is significantly associated with a reduced quality of life, a higher risk of falls and fractures and a higher risk of mortality. Sarcopenia has been moderately associated with a higher risk of reduced instrumental activities of daily living (IADL). However, the GLIS WG found only inconclusive level of evidence to support associations between sarcopenia and higher risks of hospitalization, nursing home admission, mobility impairments, and reduced basic activities of daily living (ADL). This limitation underscores the scarcity of longitudinal studies, highlighting a barrier to understanding its progression and implications over time.
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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 | 0.036 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".