Everolimus in the management of metastatic neuroendocrine tumours
Bibliographic record
Abstract
Neuroendocrine tumours are increasing in incidence and cause a variety of symptoms. The mammalian target of rapamycin (mTOR) pathway plays a key role in neuroendocrine tumour (NET) pathogenesis, leading to increased lipid synthesis, protein synthesis and cellular growth. Upregulation of this pathway is noted in both hereditary and sporadic NETs. This understanding has led to investigations of mTOR inhibitors as therapy for metastatic NETs. After promising preclinical findings, everolimus, an mTOR inhibitor, was trialled in the RADIANT-1-4 studies on patients with advanced, well differentiated NETs. RADIANT-3 and RADIANT-4 established the efficacy of everolimus in improving progression-free survival (PFS) for metastatic NET of pancreatic, lung and gastrointestinal origin, leading to the US Food and Drug Administration (FDA) approval for its use in tumour control in those settings. Everolimus treatment is generally well tolerated; common adverse events include stomatitis, diarrhoea, rash and hyperglycaemia. Although discontinuation rates are low, many patients may require dose modification to successfully continue therapy. The combination of everolimus with somatostatin analogues (SSAs) (such as octreotide or pasireotide) or other targeted agents such as bevacizumab has not produced additional incremental benefit, and dual biologic therapy is not used widely. Ongoing trials are investigating everolimus compared with chemotherapy, optimal sequencing of therapy and combination of everolimus with radiotherapy. Future research should concentrate on identification of predictive biomarkers for benefit from mTOR therapy and include quality of life as a measure.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| 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".