Small Bowel Neuroendocrine Tumors—10-Year Experience of the Ottawa Hospital (TOH)
Bibliographic record
Abstract
(1) Aim: The prevalence and incidence of small bowel NETs have increased significantly over the past two decades. This study aims to report the 10-year experience of SB-NET management at a regional cancer center in Canada. (2) Materials and methods: We conducted a retrospective study of the clinical and pathological data of patients diagnosed with biopsy-proven SB-NET at The Ottawa Hospital (TOH), Ottawa, Canada between 2011 and 2021. We report the clinicopathological characteristics of these patients, as well as their outcomes data, including survival rates. (3) Results: Between 2011 and 2021, a total of 177 SB-NET cases were identified with 51% (n = 91) of cases being males. The most common sites of the tumors were the ileum 53% (n = 94), followed by the duodenum 9% (n = 16) and jejunum 7% (n = 12). Approximately 24% (n = 42) of the patients had symptoms for over six months prior to diagnosis and 18% (n = 32) had functioning SB-NET during the course of the disease. The majority of patients had locally advanced or metastatic disease at the time of presentation with stage III, and stage IV representing 42% (n = 75), and 41% (n = 73) respectively. The majority of patients 84% (n = 148) had well-differentiated histology. One hundred twenty patients underwent surgical resection of the primary tumor including 28 patients (16%) with limited metastatic disease. A total of 21 patients (18%) had recurrence after curative surgery. A total of 62 patients (35%) received first-line somatostatin analog (SSA) therapy for unresectable disease and seven patients had PRRT after progression on SSA. Five years OS was 100%, 91%, 97%, and 73% for stages I, II, III, and IV respectively. In univariate analysis, carcinoid symptoms, T stage, and differentiation were significant predictors for worse overall survival, but not RFS. (4) Conclusions: Compared to published historical controls, our study suggests improvement in the 5-year survival rate of SB-NETs over the last 10 years.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".