Bibliographic record
Abstract
Abstract Neuroendocrine neoplasms (NENs) are heterogeneous malignancies which are becoming more common. Systemic treatment is considered for patients with advanced disease, and treatment decisions are often driven by the histological grade of the tumor and the site of primary. Somatostatin analogues are the first-line option of choice for gastroenteropancreatic NENs but subsequent options may include the targeted agents everolimus and sunitinib as well as peptide receptor radionuclide therapy. Telotristat is a new option for the treatment of refractory carcinoid syndrome diarrhea. Chemotherapy is infrequently used for Grade 1 to 2 NENs (except for the combination of capecitabine and temozolomide) but is the mainstay of therapy for Grade 3 neuroendocrine carcinomas. Bronchial NENs are graded differently and there are few proven options for systemic treatment. Optimal integration of available systemic therapies, the timely recognition of tumor heterogeneity, and the use of nuclear medicine are areas of ongoing research. Finally, the patient experience is impacted by factors such as delayed diagnosis and symptoms of carcinoid syndrome. Clinicians need to account for patient priorities and disease characteristics to individualize therapy choices for patients with advanced NEN.
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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".