Best evidence summary for nutritional management of patients with sarcopenic obesity
Bibliographic record
Abstract
ObjectiveTo summarize the best available evidence on nutritional management for patients with sarcopenic obesity,so as to provide an evidence-based foundation for developing scientific and effective nutritional intervention strategies.MethodsA comprehensive search was conducted across multiple databases,including UpToDate, DynaMed, BMJ Best Practice,CMA Infobase(Canadian Medical Association), Chinese Medical Guideline Network,National Guideline Clearinghouse(USA),WHO Guidelines Database,RNAO (Registered Nurses' Association of Ontario),CBM(Chinese Biomedical Literature Database),CNKI,Wanfang Data,VIP Database, PubMed, EMbase,Web of Science,and Cochrane Library.The retrieval period spanned from database setup to December 1,2024.Studies focusing on nutritional interventions for sarcopenic obesity were included.ResultsA total of 18 studies were included, comprising 1 clinical guideline,2 evidence-based clinical decision documents,5 expert consensus statements, 7 systematic reviews, and 3 meta-analyses.The evidence was synthesized into 32 key recommendations across four domains: screening and assessment,dietary modifications,nutritional supplementation,health education and follow-up monitoring.ConclusionsThe summarized best evidence on nutritional management for sarcopenic obesity can guide healthcare providers in safely and effectively improve patients' nutritional status,enhance quality of life,and reduce disability and mortality rates.
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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 | 0.010 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".