Sorafenib tosylate in advanced kidney cancer: past, present and future
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it