Expert Consensus Practice Recommendations of the North American Neuroendocrine Tumor Society for the management of high grade gastroenteropancreatic and gynecologic neuroendocrine neoplasms
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it