İnce Barsakta İnvajinasyona Neden Olan Leiomyosarkom: Olgu Sunumu
Bibliographic record
Abstract
Introduction: Small bowel leiomyosarcoma is an extremely rare condition among gastrointestinal malignancies. They are often asymptomatic in the early stages and are difficult to diagnose by lower and upper gastrointestinal endoscopy. Case Report: A 30-year-old male patient with a diagnosis of hemophilia presented to us with complaints of abdominal pain, nausea and vomiting. Abdominal ultrasonography and computer tomography was done; a mass lesion, approximately 5×5 cm in size, causing invagination at the ileal level was observed. After preoperative preparations, the patient was operated on; laparotomy and the existing mass lesion was removed by segmenter small bowel resection and end-to-end anastomosis. He was discharged on the postoperative 3rd day without any complications. The patient, whose histopathologically presented leiomyosarcoma, was under oncological follow-up. Discussion: Small bowel leiomyosarcomas that differentiate from gastrointestinal stromal tumors can be distinguished by various immunohistochemical staining methods. Magnetic resonance enterography, computed tomography/colonography and capsule endoscopy may be needed in the differential diagnosis. Surgical resection still maintains its importance in the approach to such tumors, and the prognosis depends on tumor size and histological stage. Keywords: gastrointestinal stromal tumor, intestinal obstruction, leiomyosarcoma
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".