A Pooled Analysis of Sequential Therapies with Sorafenib and Sunitinib in Metastatic Renal Cell Carcinoma
Bibliographic record
Abstract
OBJECTIVE: To evaluate the optimal sequence for the receptor tyrosine kinase inhibitors (rTKIs) sorafenib and sunitinib in metastatic renal cell cancer. METHODS: We performed a retrospective analysis of patients who had received sequential therapy with both rTKIs and integrated these results into a pooled analysis of available data from other publications. Differences in median progression-free survival (PFS) for first- (PFS1) and second-line treatment (PFS2), and for the combined PFS (PFS1 plus PFS2) were examined using weighted linear regression. RESULTS: In the pooled analysis encompassing 853 patients, the median combined PFS for first-line sunitinib and 2nd-line sorafenib (SuSo) was 12.1 months compared with 15.4 months for the reverse sequence (SoSu; 95% CI for difference 1.45-5.12, p = 0.0013). Regarding first-line treatment, no significant difference in PFS1 was noted regardless of which drug was initially used (0.62 months average increase on sorafenib, 95% CI for difference -1.01 to 2.26, p = 0.43). In second-line treatment, sunitinib showed a significantly longer PFS2 than sorafenib (average increase 2.66 months, 95% CI 1.02-4.3, p = 0.003). CONCLUSION: The SoSu sequence translates into a longer combined PFS compared to the SuSo sequence. Predominantly the superiority of sunitinib regarding PFS2 contributed to the longer combined PFS in sequential use.
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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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.017 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".