Exercise Interventions to Address Sarcopenia in People with Multiple Myeloma: A Scoping Review
Bibliographic record
Abstract
Background: The clinical characteristics of sarcopenia, including low muscular strength, are commonly seen among people with multiple myeloma. A scoping review was conducted to explore the role of exercise as a potential countermeasure for sarcopenia in this population. Our objectives were to (1) describe the design and findings of the studies and (2) identify the outcomes used in exercise-related studies to characterize sarcopenia. Methods: A systematic search (to March 2025) was conducted for published studies involving exercise or physical activity for individuals with multiple myeloma using key databases (MEDLINE, Embase, CINAHL, Scopus). Results: Of 971 articles reviewed, 12 articles were included, involving 967 total participants and 624 with multiple myeloma. All 12 studies included a measure for muscle physical performance, 9 studies included measures for muscular strength, and 7 studies included measures for muscle quantity/quality. Five studies reported a significant improvement from exercise for measures of muscular strength, four studies reported a significant benefit for physical performance, and three studies reported a benefit in muscle quantity. Few studies included outcomes that met all the international criteria recommended to characterize sarcopenia. Conclusions: Further multicentre research trials are needed to better understand whether and how exercise may be helpful for people with multiple myeloma, especially in the context of sarcopenia.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".