MétaCan
Menu
Back to cohort
Record W4411615921 · doi:10.1111/obr.13973

Sarcopenic Obesity in Metabolic and Bariatric Surgery: A Scoping Review

2025· review· en· W4411615921 on OpenAlexafffund
Flávio Teixeira Vieira, Carla M. Prado, Jessica Thorlakson, Carlene Johnson Stoklossa, Jennifer Jin, Lorenzo M. Donini, Leah Gramlich, Barbara Bielawska

Bibliographic record

VenueObesity Reviews · 2025
Typereview
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsUniversity of OttawaAlberta Health ServicesUniversity of Alberta
FundersAlberta InnovatesEuropean CommissionWomen and Children's Health Research InstituteCanada Research ChairsChildren's Health Research Institute
KeywordsMedicineSarcopenic obesityPsychological interventionObesityIntensive care medicineQuality of life (healthcare)Incidence (geometry)SarcopeniaWeight lossGastric bypassPhysical therapyInternal medicineNursing

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.500
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0120.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.130
GPT teacher head0.434
Teacher spread0.305 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations7
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueObesity ReviewsSame topicNutrition and Health in AgingFrench-language works237,207