An elusive consensus definition of sarcopenia impedes research and clinical treatment: A narrative review
Bibliographic record
Abstract
The definition of sarcopenia, the age-related loss of muscle mass, has evolved since the term's inception and yet there is no consensus. Many of the identified definitions of sarcopenia centre their criteria around the loss of muscle mass, loss of function, and weakness. Common variables to various definitions of sarcopenia are appendicular lean soft tissue mass (often called muscle mass), grip strength, and gait speed. However, a lack of consensus remains among operational definitions and diagnostics for this newly recognized disease and may be attributed to the absence of appropriate tools that accurately measure the outcomes of interest, such as skeletal muscle instead of lean mass. In this narrative review, we describe the evolution of the consensus groups' definition of sarcopenia, address the need for more accurate measures of muscle mass and function, and effective, low-cost treatments (i.e., resistance training and diet) for this disease. Consensus on what constitutes sarcopenia is critical to propel research in the field and, importantly, provide what prognostic value a sarcopenia diagnosis provides and how such a patient would be treated.
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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".