Outcomes of primary versus conversional Roux-En-Y gastric bypass after laparoscopic sleeve gastrectomy: a retrospective propensity score–matched cohort study
Bibliographic record
Abstract
Abstract Background Conversional surgery is common after laparoscopic sleeve gastrectomy (LSG) because of suboptimal weight loss (SWL) or poor responders and gastroesophageal reflux disease (GERD). Roux-en-Y gastric bypass (RYGB) is the most common conversional procedure after LSG. Methods A retrospective cohort study analyzed patients who underwent primary RYGB (PRYGB) or conversional RYGB (CRYGB) at three specialized bariatric centers between 2008 and 2019 and tested for weight loss, resolution of GERD, food tolerance (FT), early and late complications, and the resolution of associated medical problems. This was analyzed by propensity score matching (PSM). Results In total, 558 (PRYGB) and 155 (CRYGB) completed at least 2 years of follow-up. After PSM, both cohorts significantly decreased BMI from baseline (p < 0.001). The CRYGB group had an initially more significant mean BMI decrease of 6.095 kg/m2 at 6 months of follow-up (p < 0.001), while the PRYGB group had a more significant mean BMI decrease of 5.890 kg/m2 and 8.626 kg/m2 at 1 and 2 years, respectively (p < 0.001). Food tolerance (FT) improved significantly in the CRYGB group (p < 0.001), while CRYGB had better FT than PRYGB at 2 years (p < 0.001). A GERD resolution rate of 92.6% was recorded in the CRYGB (p < 0.001). Both cohorts had comparable rates of early complications (p = 0.584), late complications (p = 0.495), and reoperations (p = 0.398). Associated medical problems at 2 years significantly improved in both cohorts (p < 0.001). Conclusions CRYGB is a safe and efficient option in non- or poor responders after LSG, with significant weight loss and improvement in GERD. Moreover, PRYGB and CRYGB had comparable complications, reoperations, and associated medical problem resolution 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".