Sorafenib tosylate in advanced kidney cancer: past, present and future
Bibliographic record
Abstract
The objective of this paper was to review the development of sorafenib tosylate in kidney cancer. The MedLine database, the Proceedings of the Annual American Society of Clinical Oncology meeting, as well as those of other key international meetings were extensively searched to identify relevant publications. Furthermore, the authors' direct experience with the drug was taken into account when commenting on the results retrieved. Sorafenib is a multikinase inhibitor that targets VEGF and PDGF receptors, other kinases, as well as the serine-threonine kinase Raf. Following early signs of activity from phase I and II studies, it has been shown to improve survival of pretreated advanced kidney cancer patients within a placebo-controlled, randomized, phase III trial, leading to its approval both in the United States and in Europe. Its activity has been subsequently confirmed in a real-world population by two expanded access programs performed globally, but not in a first-line setting; it also proved to be non-cross-resistant with two other molecularly targeted agents. Finally, its toxicity profile, which is acceptable and highly predictable, makes sorafenib appealing for combination treatments, especially with other molecularly targeted agents. Despite having been already demonstrated to be active in kidney cancer, the exact role of sorafenib in the first-line setting, in patients who have failed other molecularly targeted agents, and especially in combination with other agents, deserves further, prospective, studies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".