Unveiling the effects of key factors in enhancing gastroesophageal reflux: A fluid-structure analysis before and after laparoscopic sleeve gastrectomy
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Gastro-oesophageal reflux disease (GERD) consists in the passage of gastric acid content from the stomach to the oesophagus, causing burns and deteriorating the quality of life. Laparoscopic Sleeve Gastrectomy (LSG) could induce de novo GERD and worsen pre-existing GERD because of the higher gastric pressurisation, reduction of stomach volume and a wider His-angle. In the proposed work, various computational gastric 2D models were developed to understand the effects of variables such as the His-angle, the antral dimension, and the bolus viscosity on the reflux increase. METHODS: Fluid-Structure Interaction (FSI) computational models which couple the solid mechanics of the gastric wall, and the fluid domain of the bolus, have been developed to shed light on biomechanical aspects of GERD after LSG. A closure was imposed to the lower oesophageal sphincter (LES) mimicking what happens physiologically after food intake. RESULTS: Results showed that the configuration prone to higher reflux flow was the post-surgical 65° model with a staple line starting directly from the pylorus without antral preservation, for all considered viscosities. Increasing viscosity, reflux flow decreased. Post-surgical refluxes were higher than pre-ones and decreased with increasing antrum preservation. CONCLUSIONS: These results could be a starting point for analysis of anatomical features, bariatric surgery and GERD occurrence. Further studies based on 3D geometries need to be performed.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".