The Impact of Gastrectomy on Inflammatory Bowel Disease Risk in Gastric Cancer Patients: A Critical Analysis
Bibliographic record
Abstract
Gastrectomy, a prevalent surgical procedure for gastric cancer, results in substantial alterations to the gastrointestinal tract, including reduced gastric acid production and significant modifications to the gut microbiota. These changes can impair postoperative recovery, influence metabolic functions, and predispose patients to inflammatory bowel disease (IBD). Studies have shown an increased risk of IBD, particularly Crohn's disease (CD) and ulcerative colitis (UC), in patients following gastrectomy and bariatric surgeries such as Roux-en-Y gastric bypass (RYGB) and sleeve gastrectomy (SG). For instance, patients undergoing RYGB have a higher hazard ratio for developing CD, while SG patients show an increased risk for UC. The surgical alteration of the gastrointestinal tract promotes dysbiosis, with a significant increase in pathogenic bacteria and a decrease in beneficial microbial populations. This dysbiosis can impair the intestinal mucosal barrier and promote systemic inflammation. Understanding the mechanisms behind these changes and their clinical implications is essential for developing effective postoperative management strategies. Probiotics and enhanced recovery after surgery (ERAS) protocols have shown promise in mitigating these adverse effects, improving gut microbiota balance, and enhancing patient outcomes. Further research is necessary to fully elucidate the long-term impacts of gastrectomy on gastrointestinal health and to refine therapeutic approaches for postoperative care.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.003 | 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".