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Record W4391886402 · doi:10.1093/jcag/gwad061.046

A46 METABOLIC OUTCOMES 12-MONTHS POST-BARIATRIC SURGERY: SLEEVE VERSUS ROUX-EN-Y GASTRIC BYPASS

2024· article· en· W4391886402 on OpenAlexaff
Sharvika Bharatselvam, Katherine J. P. Schwenger, Y Ghorbani, Sandra E. Fischer, Timothy Jackson, Allan Okrainec, Johane P. Allard

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2024
Typearticle
Languageen
FieldMedicine
TopicBariatric Surgery and Outcomes
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsGastric bypassRoux-en-Y anastomosisMedicineSleeve gastrectomySurgeryGastric bypass surgeryWeight lossGastroenterologyInternal medicineObesity

Abstract

fetched live from OpenAlex

Abstract Background Obesity and its related comorbidities, such as type 2 diabetes (T2D), hyperlipidemia, cardiovascular disease (CVD), and non-alcoholic fatty liver disease (NAFLD) are on the rise. When lifestyle modifications, such as diet and exercise, are ineffective at producing sustained weight loss and improvements in obesity-related comorbidities, other interventions may be recommended. Bariatric surgery is considered in those who have a body mass index (BMI) over 40 kg/m2 or with comorbid conditions and a BMI between 35-40 kg/m2. Two commonly used procedures within this category of surgery are Roux-en-Y gastric bypass (RYGB), the gold standard for weight loss; and laparoscopic sleeve gastrectomy (LSG). Though both surgeries are effective at inducing weight loss, more information is needed on their comparative effectiveness in improving clinical and biochemical outcomes in patients with T2D, hyperlipidemia, and NAFLD in real-world practice. Aims Therefore, our objective was to determine the overall effectiveness of RYGB versus LSG at 12-months post-surgery and specifically assess their responses in those with hyperlipidemia, T2D and NAFLD. We chose to analyze both glucose and lipid parameters along with anthropometric measures as they not only provide insight into improvements in comorbid outcomes of obesity but also little-to-no prospective studies compare all of these outcomes in one study using the same cohort. Methods This is a prospective cross-sectional and cohort study of 142 patients who underwent bariatric surgery. Clinical and biochemical data were collected at baseline, prior to surgery and 12-months post-bariatric surgery. A liver biopsy was collected during surgery to diagnose NAFLD. The main outcome was 12-month changes in lipid parameters, mainly total cholesterol, between types of surgery. Results 107 individuals underwent RYGB (75.4%) and 35 underwent LSG (24.6%). Both groups were similar at baseline except for the higher waist circumference and proportion of males undergoing LSG. At 12-months post-surgery, RYGB versus LSG resulted in a significantly lower BMI (pampersand:003C0.0001), triglycerides (pampersand:003C0.05), total and LDL cholesterol (pampersand:003C0.005). Interestingly, ALT was significantly lower in those who underwent LSG (pampersand:003C0.005). In subgroup analysis for determining the effect of the type of surgery in those with comorbidities of obesity, RYGB was superior at improving lipid-related parameters in those with hyperlipidemia with a significant decrease in total cholesterol -0.48 (-0.84, 0.34) (pampersand:003C0.05) whereas LSG was superior at reducing ALT in those with NAFLD with significant decreases in total cholesterol and LDL. Conclusions RYGB leads to greater reductions in BMI and lipid parameters, especially in those with hyperlipidemia, whereas LSG showed greater improvements in liver enzymes in those with NAFLD. Funding Agencies CIHR

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.012
GPT teacher head0.243
Teacher spread0.231 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations1
Published2024
Admission routes1
Has abstractyes

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