Sarcopenia Definition: The Position Statements of the Sarcopenia Definition and Outcomes Consortium
Bibliographic record
Abstract
OBJECTIVES: To develop an evidence-based definition of sarcopenia that can facilitate identification of older adults at risk for clinically relevant outcomes (eg, self-reported mobility limitation, falls, fractures, and mortality), the Sarcopenia Definition and Outcomes Consortium (SDOC) crafted a set of position statements informed by a literature review and SDOC's analyses of eight epidemiologic studies, six randomized clinical trials, four cohort studies of special populations, and two nationally representative population-based studies. METHODS: Thirteen position statements related to the putative components of a sarcopenia definition, informed by the SDOC analyses and literature synthesis, were reviewed by an independent international expert panel (panel) iteratively and voted on by the panel during the Sarcopenia Position Statement Conference. Four position statements related to grip strength, three to lean mass derived from dual-energy x-ray absorptiometry (DXA), and four to gait speed; two were summary statements. RESULTS: The SDOC analyses identified grip strength, either absolute or scaled to measures of body size, as an important discriminator of slowness. Both low grip strength and low usual gait speed independently predicted falls, self-reported mobility limitation, hip fractures, and mortality in community-dwelling older adults. Lean mass measured by DXA was not associated with incident adverse health-related outcomes in community-dwelling older adults with or without adjustment for body size. CONCLUSION: The panel agreed that both weakness defined by low grip strength and slowness defined by low usual gait speed should be included in the definition of sarcopenia. These position statements offer a rational basis for an evidence-based definition of sarcopenia. The analyses that informed these position statements are summarized in this article and discussed in accompanying articles in this issue of the journal. J Am Geriatr Soc 68:1410-1418, 2020.
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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 | 0.181 | 0.186 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.008 | 0.017 |
| Research integrity | 0.013 | 0.018 |
| 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".