International Clinical Practice Guidelines for Sarcopenia (ICFSR): Screening, Diagnosis and Management
Bibliographic record
Abstract
OBJECTIVES: Sarcopenia, defined as an age-associated loss of skeletal muscle function and muscle mass, occurs in approximately 6 - 22 % of older adults. This paper presents evidence-based clinical practice guidelines for screening, diagnosis and management of sarcopenia from the task force of the International Conference on Sarcopenia and Frailty Research (ICSFR). METHODS: To develop the guidelines, we drew upon the best available evidence from two systematic reviews paired with consensus statements by international working groups on sarcopenia. Eight topics were selected for the recommendations: (i) defining sarcopenia; (ii) screening and diagnosis; (iii) physical activity prescription; (iv) protein supplementation; (v) vitamin D supplementation; (vi) anabolic hormone prescription; (vii) medications under development; and (viii) research. The ICSFR task force evaluated the evidence behind each topic including the quality of evidence, the benefit-harm balance of treatment, patient preferences/values, and cost-effectiveness. Recommendations were graded as either strong or conditional (weak) as per the GRADE (Grading of Recommendations Assessment, Development and Evaluation) approach. Consensus was achieved via one face-to-face workshop and a modified Delphi process. RECOMMENDATIONS: We make a conditional recommendation for the use of an internationally accepted measurement tool for the diagnosis of sarcopenia including the EWGSOP and FNIH definitions, and advocate for rapid screening using gait speed or the SARC-F. To treat sarcopenia, we strongly recommend the prescription of resistance-based physical activity, and conditionally recommend protein supplementation/a protein-rich diet. No recommendation is given for Vitamin D supplementation or for anabolic hormone prescription. There is a lack of robust evidence to assess the strength of other treatment options.
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 | 0.022 | 0.079 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".