Sunitinib re-challenge in advanced renal-cell carcinoma
Bibliographic record
Abstract
Despite offering significant clinical benefits in advanced renal-cell carcinoma (RCC), the effectiveness of targeted therapies eventually declines with the development of resistance. Defining optimal sequences of therapy is therefore the focus of much current research. There is also evidence that treatment 're-challenge' may be an effective strategy in some patients. We review evidence to evaluate whether sunitinib may have value as re-challenge therapy in patients who have progressed on prior targeted therapy with sunitinib and/or an alternative tyrosine kinase inhibitor or mammalian target of rapamycin inhibitor. Re-challenge with sunitinib appears to be of clinical benefit, thus representing a feasible therapeutic option for patients with advanced RCC who are refractory to other treatments and are able to receive further therapy. These observations support hypotheses that resistance to targeted agents is transient and can be at least partially reversed by re-introduction of the same agent after a treatment break. Median progression-free survival durations appear to be shorter and response rates lower on re-challenge than following initial treatment, although a wider interval between treatments appears to increase response to sunitinib re-challenge.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| 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 | 0.002 |
| 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 teacher head, 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".