The outcomes of Re-Redo bariatric surgery—results from multicenter Polish Revision Obesity Surgery Study (PROSS)
Bibliographic record
Abstract
Abstract The increasing prevalence of bariatric surgery has resulted in a rise in the number of redo procedures as well. While redo bariatric surgery has demonstrated its effectiveness, there is still a subset of patients who may not derive any benefits from it. This poses a significant challenge for bariatric surgeons, especially when there is a lack of clear guidelines. The primary objective of this study is to evaluate the outcomes of patients who underwent Re-Redo bariatric surgery. We conducted a retrospective cohort study on a group of 799 patients who underwent redo bariatric surgery between 2010 and 2020. Among these patients, 20 individuals underwent a second elective redo bariatric surgery (Re-Redo) because of weight regain (15 patients) or insufficient weight loss, i.e. < 50% EWL (5 patients). Mean BMI before Re-Redo surgery was 38.8 ± 4.9 kg/m2. Mean age was 44.4 ± 11.5 years old. The mean %TWL before and after Re-Redo was 17.4 ± 12.4% and %EBMIL was 51.6 ± 35.9%. 13/20 patients (65%) achieved > 50% EWL. The mean final %TWL was 34.2 ± 11.1% and final %EBMIL was 72.1 ± 20.8%. The mean BMI after treatment was 31.9 ± 5.3 kg/m2. Complications occurred in 3 of 20 patients (15%), with no reported mortality or need for another surgical intervention. The mean follow-up after Re-Redo was 35.3 months. Although Re-Redo bariatric surgery is an effective treatment for obesity, it carries a significant risk of complications.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 | 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".