Sarcopenic Obesity in Metabolic and Bariatric Surgery: A Scoping Review
Bibliographic record
Abstract
The risk of sarcopenic obesity (SO), characterized by the coexistence of excess adiposity and low muscle mass and function, may be increased in metabolic and bariatric surgery (MBS). There is a possibility of SO development after surgery, but also aggravation of pre-existing SO, a hidden condition associated with poor health-related outcomes. This scoping review synthesizes existing literature on SO in MBS, with a thorough discussion of diagnostic criteria and assessment methods, investigation of SO prevalence (presurgery and postsurgery), incidence postsurgery, and impact on clinical outcomes. SO prevalence in MBS is highly heterogeneous, depending on the applied diagnostic criteria and body composition/physical function assessments. Following appropriate diagnostic criteria, one of four individuals both before and post-MBS seems to have SO, thus requiring targeted interventions. SO may be associated with lower weight loss and quality of life, increased risk of gastric leak, prolonged operation time, and hospital stay. Increased awareness of postsurgery SO is recommended, especially with aging. Standardization of SO diagnosis is urgently needed to improve identification and enable comparisons among studies and associations with clinical outcomes. This is important for developing effective policies, guidelines, and interventions to better address and manage this condition.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".