Ultrasound for skeletal muscle assessment in surgical oncology: A scoping review
Bibliographic record
Abstract
BACKGROUND: Skeletal muscle wasting in cancer patients is associated with adverse outcomes. Ultrasound offers a non-invasive muscle assessment, but no previous review has focused on its application during perioperative period. This scoping review aims to map the current literature on the ultrasound use for skeletal muscle assessment in cancer patients during the perioperative period and identify knowledge gaps for future research. METHODS: A systematic literature search was conducted in the Cochrane Central Register of Controlled Trials, the Cochrane Database of Systematic Reviews, Embase, Medline, and Web of Science. Two independent reviewers screened studies for eligibility and extracted relevant data, including study characteristics, population, ultrasound protocols, and outcomes. Inclusion criteria were primary studies involving adults undergoing cancer surgery with ultrasound used for skeletal muscle assessment during the perioperative period. RESULTS: Thirteen studies were included. The majority assessed quantitative parameters, with the rectus femoris muscle being the most evaluated. Muscle thickness and cross-sectional area were the most frequently reported parameters. Studies validated ultrasound parameters against established tools and clinical indicators, including sarcopenia, frailty, muscle strength, and biomarker. Ultrasound was also used to predict postoperative outcomes and assess perioperative interventions. However, variability in ultrasound protocols highlights the need for standardized practices, and the lack of consensus on cutoffs warrants future research. CONCLUSIONS: This review demonstrated the validity and the applications of ultrasound for skeletal muscle assessment in cancer patients during the perioperative period. Significant variability in ultrasound protocols and the absence of standardized cutoffs highlight the need for further research.
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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 | 0.014 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".