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
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 it