Sorafenib in the Management of Metastatic Renal Cell Carcinoma
Bibliographic record
Abstract
PURPOSE: Sorafenib represents one of the two standards of care for patients with metastatic renal cell carcinoma (mRCC). In the present review, we provide information regarding the use of sorafenib in first and second lines. We also describe results for dose escalation strategies. Finally, we provide data addressing the efficacy of sorafenib in patients with mRCC of non-clear-cell histology. RECENT FINDINGS: Sorafenib is a valid first-line agent. Sorafenib response rates and toxicity are not affected by patient age or site of metastasis. The sequence of first-line sorafenib followed by second-line sunitinib resulted in a longer duration of response than did the opposite sequence. Sorafenib efficacy in first-line therapy can be potentiated by co-administration of low-dose interferon. Moreover, in first-line therapy, impressive response rates were recorded when the dose of sorafenib was escalated beyond the standard 400 mg twice daily. Similarly impressive response rates were observed with dose escalation in second-line therapy. It is notable that dose escalation after failure of standard sorafenib dose also prolongs progression-free survival. Finally, the efficacy of sorafenib is not limited to clear-cell histology, but also applies to chromophobe and papillary mRCC variants. SUMMARY: Sorafenib is a highly effective and well-tolerated agent for first- and second-line patients with clear-cell, chromophobe, or papillary mRCC variants.
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 | 0.001 |
| 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 | 0.001 |
| 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".