A rapid review of nutrition and exercise approaches to managing unintentional weight loss, muscle loss, and malnutrition in cancer
Bibliographic record
Abstract
PURPOSE: This narrative review summarizes the evidence for nutrition, exercise, and multimodal interventions to maintain weight and muscle mass and prevent malnutrition from meta-analysis, systematic reviews, and randomized controlled trials published within the last 5 years, and in comparison to future research priority areas identified by international guidelines. RECENT FINDINGS: Dietary counseling with oral nutrition support (ONS), escalated to enteral nutrition if weight loss continues, is the gold standard treatment approach to maintaining weight and preventing malnutrition. Recent ONS trials with dietary counseling show promising findings for weight maintenance, extending the literature to include studies in chemoradiotherapy, however, change in body composition is rarely evaluated. Emerging trials have evaluated the impact of isolated nutrients, amino acids, and their derivatives (ie, β-hydroxy β-methylbutyrate) on muscle mass albeit with mixed effects. There is insufficient evidence evaluating the effect of exercise interventions on unintentional weight loss, muscle mass, and malnutrition, however, our knowledge of the impact of multimodal nutrition and exercise interventions is advancing. Prehabilitation interventions may attenuate weight and muscle loss after surgery, particularly for patients having gastrointestinal and colorectal surgery. Multimodal trials that commence during treatment show mixed effects on weight and muscle mass when measured. SUMMARY: This review highlights that the evidence for preventing unintentional weight loss and malnutrition from cancer treatment is strong within nutrition. Multimodal interventions are emerging as effective interventions to prevent unintentional weight loss. Promising interventions are demonstrating improvements in muscle mass, however further exploration through studies designed to determine the effect on muscle is required.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".