Comparing the Treatment and Side Effects of Existing Bariatric Surgery Procedures: An Observational Study
Bibliographic record
Abstract
Abstract Importance Bariatric surgery is an established treatment for obesity and its associated comorbidities, including diabetes, hypertension, sleep apnea, and hypercholesterolemia. Despite the widespread adoption of various bariatric procedures, rigorous causal comparisons of their differential effects on treatment outcomes and adverse events remain scarce. Objective This large-scale observational study aimed to rigorously compare the effects of commonly performed bariatric surgery procedures on both weight loss (effevtiveness) and the risk of postoperative complications. Evidence Review This study utilized data from the American College of Surgeons Metabolic and Bariatric Surgery Accreditation and Quality Improvement Program (MB-SAQIP) database, encompassing 729,482 cases from 2015 to 2020. With Sleeve Gastrectomy serving as the reference procedure, we assessed the effect of alternative procedures on changes in body mass index (BMI) and the risk of reoperation, readmission, and subsequent interventions. State-of-the-art machine learning-based causal inference techniques, including Causal Forest, Dragonnet, and Double Machine Learning, were employed to conduct robust causal comparisons. Findings Biliopancreatic Diversion with Duodenal Switch (BPD/DS) demonstrated superior BMI reduction compared with Sleeve Gastrectomy. Roux-en-Y Gastric Bypass (RYGB), Adjustable Gastric Band (AGB, or Band), and Single Anastomosis Duodeno-Ileal Bypass with Sleeve Gastrectomy (SADI-S) were associated with less pronounced BMI decreases relative to Sleeve Gastrectomy. The risk of complications was similar or higher for all other surgical procedures compared with Sleeve Gastrectomy. Importantly, these represent causal effect estimates rather than mere associations, providing clinically actionable evidence for treatment selection. Detailed effect estimates and risk ratios, along with their confidence intervals, are presented in the full text. All our implementations are available at GitHub. Conclusions and Relevance Our causal estimates–derived from state-of-the-art machine learning methods applied to the largest bariatric surgery registry–provide the first rigorous quantitative evidence supporting current clinical practice guidelines, issued by the American Society for Metabolic and Bariatric Surgery (ASMBS), and enable evidence-based surgical decision-making. Key Points Question What are the causal effects of the five widely adopted bariatric surgery procedures on weight loss efficacy and postoperative complication risks? Findings Our causal analysis reveals that Biliopancreatic Diversion with Duodenal Switch (BPD/DS) achieves significantly greater BMI reduction compared with the most widely conducted Sleeve Gastrectomy, but at the cost of substantially elevated complication risks. Our causal analysis results of all five bariatric surgery procedures align with mechanistic understanding and provide quantitative causal estimates rather than associations. Meaning This represents the first large-scale and comprehensive causal analysis comparing weight loss and adverse event risks across the five most important bariatric surgery procedures, providing rigorous evidence to inform surgical decision-making.
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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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".