Management of Failed Laparoscopic Roux-en-Y Gastric Bypass
Bibliographic record
Abstract
Background: Laparoscopic Roux-en-Y gastric bypass (LRYGB) has emerged as the gold standard for the management of morbid obesity. Accordingly, patients who fail to lose weight after LRYGB present a difficult problem for the bariatric surgeons. A literature review was performed to evaluate the management options for this select bariatric population. Methods: A literature search was conducted in the EMBASE and MEDLINE databases using the most comprehensive timeline. All relevant articles were identified and full texts were obtained and reviewed. Results: Thirteen articles were retrieved based on key word searches. Management for weight failure following LRYGB included revision using the following options: laparoscopic adjustable gastric banding, pouch/anastomotic revision with or without endoluminal techniques, laparoscopic distal Roux-en-Y gastric bypass, and laparoscopic biliopancreatic diversion with duodenal switch. Laparoscopic sleeve gastrectomy may be considered in patients who fail LRYGB with nutritional deficiencies. Conclusion: Failed LRYGB should be managed based on the patient presentation and diagnostic evaluation. Patients may present with significant nutritional deficiencies/complications, failure to lose weight, or weight recidivism. A treatment algorithm is proposed based on the literature to guide bariatric surgeons with respect to management options. However, given the paucity of research with respect to this problem, additional studies are needed to provide more insight on the optimal surgical management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".