Chronic Infection and Enterocutaneous Fistula Secondary to Mesh Migration and Erosion into the Small Bowel
Bibliographic record
Abstract
Background: The use of prosthetic mesh is associated with a lower risk of hernia recurrence and hence is used commonly in hernia repair. Mesh migration and erosion into the gastrointestinal tract, albeit rare, is one of the most serious and challenging complications of hernia operations. Timely surgical intervention is an essential part in the management of this condition. Case Presentation: A 55-year-old Caucasian female was admitted with severe abdominal pain and chronic abdominal wall infection nine years after an incisional hernia repair with mesh. Computed tomography (CT) scan of the abdomen revealed mesh eroded into the small bowel with a surrounding abscess and a fistulous tract. Surgery involved resection of the small bowel segment containing the mesh followed by primary anastomosis and complete excision of the remaining mesh. An adnexal pelvic mass was also excised. Despite a post-operative surgical site infection the patient was well at follow-up. Conclusion: This report emphasizes the importance of timely surgical intervention in patients with mesh erosion into the gastrointestinal tract. Moreover, surgeons should be aware of the increased risks of mesh erosion and enterocutaneous fistula if native tissue is not interposed between the mesh and the bowel in underlay and sub-fascial hernia repair techniques.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".