Malignant duodenal gastrointestinal neuroectodermal tumor (GNET): Case report and review of the literature
Bibliographic record
Abstract
INTRODUCTION: Malignant gastrointestinal neuroectodermal tumor (GNET) is a rare malignancy primarily affecting the gastrointestinal tract. Upon cross-sectional imaging, it can be easily confused with other mesenchymal tumors. This article presents a case of duodenal GNET and reviews the current literature on this rare entity. PRESENTATION OF CASE: A 73-year-old female patient presented with a 4 cm duodenal mass on CT scan. With a presumptive diagnosis of GIST, a D3-D4 duodenectomy with cholecystectomy were performed. Subsequent pathological analysis of the surgical specimen revealed a 4.5 cm malignant gastrointestinal neuroectodermal tumor (GNET), also known as clear cell sarcoma-like gastrointestinal tumor (CCSLGT). DISCUSSION: While there are less than 115 cases of GNET reported worldwide, prognosis is usually poor with a 50 % survival at 3 years, and mortality rate described is as high as 75 %. To the authors' knowledge, this duodenal GNET case represents the first one ever described for this location. CONCLUSION: Early recognition of GNET is essential due to its poor prognosis and its ability to metastasize. Awareness of its existence and diagnostic criteria by every member of the medical team is key to obtain optimal patient care.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".