An evidence-based guide to the selection of sequential therapies in metastatic renal cell carcinoma
Bibliographic record
Abstract
Targeted therapies have introduced a paradigm shift in the management of metastatic renal cell carcinoma. Currently, four molecules (sunitinib, pazopanib, bevacizumab plus interferon, temsirolimus) are considered in first-line therapy, and three other molecules for second, or subsequent lines of therapy (everolimus, axitinib, sorafenib). In addition, other molecules and sequencing schemes are being tested in ongoing phase II/III studies. We conducted a systematic review using PubMed and several other databases up to December 2011 of prospective and retrospective studies on treatment management of metastatic renal cell carcinoma using targeted therapies, with a special focus on use of sequential treatment. Based on phase III data, the optimal sequencing scheme for patients with clear cell or even non-clear cell histological subtype appears to consist of sunitinib, followed by axitinib, followed by everolimus. Subsequent treatment options rely on lower evidence studies and could consist of fourth-line sorafenib or sunitinib rechallenge. Such therapies would qualify as last recourse options. In another context, temsirolimus may be used in patients who fulfill the Memorial Sloan-Kettering Cancer Center poor risk criteria or who have poor performance status. We conclude that in the current setting, sequential therapy represents the cornerstone of effective management of metastatic renal cell carcinoma.
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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 | 0.025 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".