Decreasing complication rates for one-stage conversion band to laparoscopic sleeve gastrectomy: A retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Laparoscopic adjustable gastric banding (LAGB) revision surgery is often necessary because of its high failure rate. The objective of this study was to demonstrate that better patient selection, when converting a failed LAGB to a laparoscopic sleeve gastrectomy (LSG) as a one-stage revision procedure, is safe, feasible and improves the complication rate. PATIENTS AND METHODS: A retrospective chart review was performed on patients who underwent a one-stage conversion of failed gastric banding to a LSG. Collected data included age, sex, body mass index (BMI), intraoperative complications, length of stay and post-operative complications. The results were compared to a previous study of 90 cases of LSG as a revision procedure for failed LAGB. RESULTS: (32-66). Seventy patients (93.3%) were operated for insufficient weight loss and 5 patients (6.7%) for intolerance to the band. In our previous study, 35 patients (39%) were operated for slippage, erosion or obstruction and 14 (15.6%) had post-operative complications as opposed to only 4 patients (5.3%) in this series (P = 0.0359). Gastric leak also improved to 1.3% compared to 5.5% previously. Average hospitalisation time was 2.5 days (1-40). CONCLUSIONS: Rigorous patient selection, without band complications such as slippage, erosion or obstruction, allows for a significantly lower rate of operative complications for a one-stage conversion of failed gastric banding to a LSG.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".