Sunitinib in metastatic renal cell carcinoma patients with brain metastases
Bibliographic record
Abstract
BACKGROUND: In a broad patient population with metastatic renal cell carcinoma (RCC), enrolled in an open-label, expanded access program (EAP), the safety profile of sunitinib was manageable, and efficacy results were encouraging. Here, the authors report results for patients with baseline brain metastases participating in this global EAP. METHODS: Previously treated and treatment-naive metastatic RCC patients ≥18 years received sunitinib 50 mg orally, once daily, on Schedule 4/2. Safety was assessed regularly, tumor measurements done per local practice, and survival data collected where possible. Analyses were done in the modified intention-to-treat (ITT) population, consisting of all patients who received ≥1 dose of sunitinib. RESULTS: As of December 2007, 4564 patients had enrolled in 52 countries. Of these enrollees, 4371 were included in the modified ITT population, of whom 321 (7%) had baseline brain metastases and had received a median of 3 treatment cycles (range 1-25). Reasons for their discontinuation included lack of efficacy (32%) and adverse events (8%). The most common grade 3-4 treatment-related adverse events were fatigue and asthenia (both 7%), thrombocytopenia (6%), and neutropenia (5%), the incidence of which were comparable to that for the overall EAP population. Of 213 evaluable patients, 26 (12%) had an objective response. Median progression-free survival and overall survival were 5.6 months (95% CI, 5.2-6.1) and 9.2 months (95% CI, 7.8-10.9), respectively. CONCLUSIONS: In patients with brain metastases from RCC, the safety profile of sunitinib was comparable to that in the general metastatic RCC population, and sunitinib showed evidence of antitumor activity.
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 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) | 0.000 | 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 | 0.000 |
| 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 teacher head, 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".