Multimodal interventions for cachexia management
Bibliographic record
Abstract
BACKGROUND: Cachexia (disease-related wasting) is a complex metabolic syndrome which occurs in people with chronic illnesses, including cancer, HIV/AIDS, kidney disease, heart disease, and chronic obstructive pulmonary disease (COPD). People with cachexia experience unintentional weight loss, muscle loss, fatigue, loss of appetite, and reduced quality of life. Multimodal interventions which work synergistically to treat the syndrome could lead to benefits. OBJECTIVES: To assess the benefits and harms of multimodal interventions aimed at alleviating or stabilising cachexia in people with a chronic illness. SEARCH METHODS: We searched CENTRAL, MEDLINE, Embase, PsycINFO, and two trials registers in July 2024, together with reference checking, citation searching, and contact with study authors to identify studies. SELECTION CRITERIA: We included randomised controlled trials (RCTs) in adults with or at risk of cachexia, comparing multimodal interventions combining two or more modalities (of pharmacology, nutrition, exercise) to treatment as usual, variation of the intervention, or unimodal intervention. DATA COLLECTION AND ANALYSIS: Two review authors independently screened potentially eligible studies, extracted data, and assessed risk of bias (RoB 1). Primary outcomes were physical function, strength, and adverse events. Secondary outcomes were body composition and weight, quality of life (QoL), appetite, fatigue, and biochemical markers. We assessed the certainty of evidence with GRADE. MAIN RESULTS: = 79%; 3 studies, 411 participants), but the evidence is very uncertain. AUTHORS' CONCLUSIONS: The review found insufficient evidence to support or refute the use of multimodal interventions in managing cachexia. The certainty of the evidence was very low. Methodologically rigorous, well-powered RCTs with adequate interaction times are needed to assess the effectiveness of multimodal interventions in managing cachexia across chronic illnesses.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".