Bibliographic record
Abstract
BACKGROUND: Until recently, few treatments were available for renal cell carcinoma (RCC) and gastrointestinal stromal tumors (GIST). Several targeted agents inhibiting key pathogenetic pathways have since been developed for RCC (sunitinib, sorafenib, bevacizumab, temsirolimus, everolimus) and GIST (imatinib, sunitinib). Sunitinib is a multi-kinase inhibitor of VEGFR-2, PDGFR (alpha,beta), FLT-3, KIT, CSF-1 and RET. OBJECTIVE: To summarize the literature regarding the structure, pharmacokinetics, pharmacodynamics, toxicity and current clinical use of sunitinib. Other potential roles for this drug in RCC, GIST and other tumor types will be discussed. METHODS: A literature search identified relevant (pre)clinical studies of sunitinib and other relevant agents. RESULTS/CONCLUSIONS: Sunitinib revolutionized the management of advanced RCC and GIST. With the realization that cross-resistance between targeted agents is incomplete, evolving strategies include sequential treatment, concurrent treatment, and biomarker development. Sunitinib also shows promise in several other tumor types that lack therapeutic options. What remains less clear is its role in tumors that are not heavily dependent on a central pathogenetic pathway, especially if effective cytotoxic therapies exist. Future clinical trials will clarify whether there is a role for sunitinib in these tumors, possibly in combination with cytotoxic agents.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".