Biomarkers of sarcopenia in clinical trials—recommendations from the International Working Group on Sarcopenia
Bibliographic record
Abstract
Sarcopenia, the age-related skeletal muscle decline, is associated with relevant clinical and socioeconomic negative outcomes in older persons. The study of this phenomenon and the development of preventive/therapeutic strategies represent public health priorities. The present document reports the results of a recent meeting of the International Working Group on Sarcopenia (a task force consisting of geriatricians and scientists from academia and industry) held on June 7-8, 2011 in Toulouse (France). The meeting was specifically focused at gaining knowledge on the currently available biomarkers (functional, biological, or imaging-related) that could be utilized in clinical trials of sarcopenia and considered the most reliable and promising to evaluate age-related modifications of skeletal muscle. Specific recommendations about the assessment of aging skeletal muscle in older people and the optimal methodological design of studies on sarcopenia were also discussed and finalized. Although the study of skeletal muscle decline is still in a very preliminary phase, the potential great benefits derived from a better understanding and treatment of this condition should encourage research on sarcopenia. However, the reasonable uncertainties (derived from exploring a novel field and the exponential acceleration of scientific progress) require the adoption of a cautious and comprehensive approach to the subject.
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 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 | 0.274 | 0.322 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.012 | 0.012 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.012 | 0.006 |
| Research integrity | 0.025 | 0.020 |
| Insufficient payload (model declined to judge) | 0.004 | 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".