Management of Small Bowel Neuroendocrine Tumors
Bibliographic record
Abstract
Several important landmark trials have reshaped the landscape of non-surgical management of small bowel neuroendocrine tumors over the last few years, with the confirmation of the antitumor effect of somatostatin analogue therapy in PROMID and CLARINET trials as well as the advent of therapies with significant potential such as mammalian target of rapamycin inhibitor (mTor) everolimus (RADIANT trials) and peptide receptor radionuclide therapy (PRRT) with 177-Lutetium (NETTER-1 trial). This narrative summarizes the recommended management strategies of small bowel neuroendocrine tumors. We review the main evidence behind each recommendation as well as compare and contrast four major guidelines, namely the 2016 Canadian Consensus guidelines, the 2017 North American Neuroendocrine Tumor Society guidelines, the 2018 National Comprehensive Cancer Network guidelines, and the 2016 European Neuroendocrine Tumor Society guidelines. Different clinical situations will be addressed, from loco-regional therapy to metastatic unresectable disease. Carcinoid syndrome, which is mostly managed by somatostatin analogue therapy and the serotonin antagonist telotristat etiprate for refractory diarrhea, as well as neuroendocrine carcinoma will be reviewed. However, several questions remain unanswered, such as the optimal management of neuroendocrine carcinomas or the effect of combining and sequencing of the aforementioned modalities where more randomized controlled trials are needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".