Expert Consensus Practice Recommendations of the North American Neuroendocrine Tumor Society for the management of high grade gastroenteropancreatic and gynecologic neuroendocrine neoplasms
Bibliographic record
Abstract
High-grade neuroendocrine neoplasms are a rare disease entity and account for approximately 10% of all neuroendocrine neoplasms. Because of their rarity, there is an overall lack of prospectively collected data available to advise practitioners as to how best to manage these patients. As a result, best practices are largely based on expert opinion. Recently, a distinction was made between well-differentiated high-grade (G3) neuroendocrine tumors and poorly differentiated neuroendocrine carcinomas, and with this, pathologic details, appropriate imaging practices and treatment have become more complex. In an effort to provide practitioners with the best guidance for the management of patients with high-grade neuroendocrine neoplasms of the gastrointestinal tract, pancreas, and gynecologic system, the North American Neuroendocrine Tumor Society convened a panel of experts to develop a set of recommendations and a treatment algorithm that may be used by practitioners for the care of these patients. Here, we provide consensus recommendations from the panel on pathology, imaging practices, management of localized disease, management of metastatic disease and surveillance and draw key distinctions as to the approach that should be utilized in patients with well-differentiated G3 neuroendocrine tumors vs poorly differentiated neuroendocrine carcinomas.
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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 | 0.046 | 0.113 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.014 | 0.015 |
| Insufficient payload (model declined to judge) | 0.008 | 0.008 |
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".