Well-Differentiated Grade 3 Neuroendocrine Tumors
Bibliographic record
Abstract
OBJECTIVES: We evaluated a population-based cohort of metastatic well-differentiated grade 3 gastroenteropancreatic neuroendocrine tumors (G3 NETs) to describe their characteristics, prognosis, and treatment outcomes. METHODS: The British Columbia provincial database was queried for G3 NETs diagnosed 2004 to 2021, and charts were reviewed to describe clinical features and outcomes. RESULTS: Forty-one patients were identified, most were diagnosed with pancreatic (58.5%) or midgut (26.8%) primary tumor and Ki-67 was less than 55% in 68.3%. The primary was resected in 19 (46.3%) with median disease-free survival of 25.2 months. Once metastatic, patients received a median of one line of systemic therapy. Median overall survival with metastatic disease was 33.8 months. Median progression-free survival was longest in patients treated with capecitabine-temozolomide (20.6 months) or somatostatin analogs (7.9 months), while etoposide-platinum provided little benefit (2.4 months). Limited data of efficacy for targeted therapies and radionuclide therapy was available. Seven patients (17.1%) were also treated with local therapies, which were associated with improved overall survival (median not reached, hazard ratio, 0.23; P = 0.012). CONCLUSIONS: Capecitabine-temozolomide and somatostatin analogs were associated with clinically meaningful benefit, and use of local therapies provided benefits in selected patients. Multidisciplinary discussion is essential to optimize individual outcomes in this heterogeneous population.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".