Effects of different techniques of endoscopic gastroplasty on weight loss among patients with obesity: a randomized, single-center pragmatic trial
Bibliographic record
Abstract
BACKGROUND AND AIMS: Obesity is a global pandemic requiring effective interventions. Endoscopic bariatric therapies are minimally invasive alternatives to surgery. We compared 3 techniques of endoscopic gastroplasty (EG): endoscopic sleeve gastroplasty (ESG) (Apollo OverStitch SX; Boston Scientific, Marlborough, Mass, USA), endoluminal vertical gastroplasty (EVG) (Endomina; EndoTools Therapeutics, Gosselies, Belgium), and primary obesity surgery endoluminal-2 (POSE-2) (Incisionless Operating Platform; USGI Medical, San Clemente, Calif, USA). METHODS: This was a single-center, randomized pragmatic study (ClinicalTrials.govNCT04854317) involving patients who underwent EG through ESG, EVG, or POSE-2. The primary end point was the percentage of total body weight loss (TBWL), with secondary end points assessing excess weight loss (EWL), safety, feasibility, anthropometric changes, metabolic, and quality of life (QoL) improvements. RESULTS: ) underwent EG. Follow-up rates were 56% at 6 months, 32% at 12 months, and 15% at 18 months. At 6, 12, and 18 months, patients experienced, respectively, TBWL of 15.5% ± 6.0%, 14.5% ± 8.5%, and 17.1% ± 10.2%, respectively, and EWL of 39.3% ± 15.5%, 36.7% ± 21.4%, and 43.0% ± 26.6%, with no statistically significant differences among the 3 techniques for both parameters (P ≥ .36). The technical success rate was 100%. The serious adverse event rate was 1.1%. Anthropometric measurements, body composition, fatty liver disease, and hyperlipidemia improved at 6-, 12-, and 18-month follow-up, as well as the QoL measured by bariatric analysis and reporting outcome system and SIO-Obesity correlated Disability Test (P < .01). CONCLUSIONS: This study underscores the potential of EG through ESG, EVG, and POSE-2 as equally effective, safe, and feasible interventions for managing obesity at a medium-term follow-up. The results were consistent despite incomplete follow-up, the main study limitation, assuming that data were missing at random.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".