Everolimus in the management of metastatic neuroendocrine tumours
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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