Prognostic Factors Associated with the Response to Sunitinib in Patients with Metastatic Renal Cell Carcinoma
Bibliographic record
Abstract
OBJECTIVE: We investigated the prognostic clinicopathologic factors associated with overall survival (os) and progression-free survival (pfs) in the once-daily continuous administration of first-line sunitinib in a consecutive cohort of Turkish patients with metastatic renal cell carcinoma (rcc). METHODS: The study enrolled 77 Turkish patients with metastatic rcc who received sunitinib in a continuous once-daily dosing regimen between April 2006 and April 2011. Univariate analyses were performed using the log-rank test. RESULTS: Median follow-up was 18.5 months. In univariate analyses, poor pfs and os were associated with 4 of the 5 factors in the Memorial Sloan-Kettering Cancer Center (mskcc) score: Eastern Cooperative Oncology Group performance status of 2 or higher, low hemoglobin, high corrected serum calcium, and high lactate dehydrogenase. In addition to those factors, hypoalbuminemia, more than 2 metastatic sites, liver metastasis, non-clear cell histology, and the presence of sarcomatoid features on pathology were also associated with poor pfs; and male sex, hypoalbuminemia, prior radiotherapy, more than 2 metastatic sites, lung metastasis, nuclear grade of 3 or 4 for the primary tumour, and the presence of sarcomatoid features were also associated with poorer os. The application of the mskcc model distinctly separated the pfs and os curves (p < 0.001). CONCLUSIONS: Our study identified prognostic factors for pfs and os with the use sunitinib as first-line metastatic rcc therapy and confirmed that the mskcc model still appears to be valid for predicting survival in metastatic rcc in the era of molecular targeted therapy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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 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".