How do we define and measure sarcopenia? A meta-analysis of observational studies
Bibliographic record
Abstract
OBJECTIVE: this study aimed to investigate how sarcopenia has been defined and measured in the literature reporting its prevalence, and how different definitions and measurement tools can affect prevalence estimates. DESIGN: systematic review and meta-analysis. SETTING AND PARTICIPANTS: community-dwelling older people. METHODS: meta-analysis of data collected from observational studies. We performed an electronic search in five databases to identify studies reporting the prevalence of sarcopenia. We used descriptive statistics to present data pertaining sarcopenia definition and measurement tools, and the quality-effects model for meta-analysis of pooled prevalence. RESULTS: we found seven different operational definitions for sarcopenia and a variety of tools applied to assess the sarcopenic markers; muscle mass, muscle strength and physical performance. The prevalence of sarcopenia varied between the definitions with general estimates ranging from 5% based on the European Working Group on Sarcopenia in Older People (EWGSOP1) criterion to 17% with the International Working Group on Sarcopenia. According to the tool used to assess muscle mass, strength and physical performance, prevalence values also varied within definitions extending from 1 to 7%, 1 to 12% and 0 to 22%, respectively. CONCLUSION AND IMPLICATIONS: the criteria used to define sarcopenia, as well as the measurement tools applied to assess sarcopenic markers have influence in the prevalence of sarcopenia. The establishment of a unique definition for sarcopenia, the use of methods that guarantee an accurate evaluation of muscle mass and the standardisation of measurement tools are necessary to allow a proper diagnosis and comparison of sarcopenia prevalence among populations.
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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 | 0.106 | 0.204 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.049 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".