Surgical Management of Small Bowel Neuroendocrine Tumour Symposia
Bibliographic record
Abstract
While rare, the incidence of small bowel neuroendocrine tumours (SBNETs) is increasing and they are now the most frequently encountered small bowel tumour [ 1 ]. Up to one quarter of SBNETs present acutely, many present as an incidentally, and most present with metastatic stage IV disease [ 2 ]. Neuroendocrine tumours usually require consultative, multidisciplinary management at specialized centres. Prolonged survival with good quality of life can be achieved in patients even with extensive metastatic disease. However, with the rising incidence and the presentation of complications from advanced disease such as obstruction, ischemia, perforation and bleeding, many of the patients with SBNET will be encountered by every general and sub-specialty surgeon undertaking acute and emergency surgery. The dilemmas that are faced by the operating surgeon who encounters this disease include: 1. How to manage of the SBNET presenting with acute complications such as obstruction and perforation? 2. The extent and technique of regional lymphadenectomy, how to do it safely? 3. What are the significance and the management of multifocal disease? 4. What is the recommended management of the primary and regional disease in patients with distant (predominately liver) metastatic disease? 5. Finally, what is the best palliation and management for the rare patients with unresectable SBNET? The following symposium attempts to answer and address these practical issues faced by the operation surgeon in patients with SBNETs. In this issue of the World Journal of Surgery, five surgeons from Canada and New Zealand present a symposia of concise reviews summarizing the management options for patients presenting with complex SBNET disease.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".