Predicting efficacy of sunitinib in metastatic renal cell carcinoma
Bibliographic record
Abstract
Abstract: Renal cell carcinoma accounts for 3% of all solid neoplasms in adults. Once poorly treated due to its poorly understood pathogenesis, the discovery of the role played by the vascular endothelial growth factor pathway in renal cell carcinoma has led to development of a number of targeted therapies that have impacted on the natural history of the disease. One of the problems related to this apparent abundance of therapies is choice of a drug tailored to the individual patient. Of all the drugs available, sunitinib accounts for more than 50% of first-line therapy. Defining which group of patients will benefit from sunitinib using a predictive biomarker would be of great help for its clinical activity. Local efforts have identified biomarkers that are potentially predictive of the efficacy of sunitinib in patients with metastatic renal cancer, being either clinical (hypertension), cellular (such as circulating endothelial cells, circulating tumoral cells), or molecular (cytokines such as vascular endothelial growth factor, hepatocyte growth factor/scatter factor), but there is a desperate need to increase the numbers of patients in the studies being conducted to provide more valid and reproducible data of use in clinical decision-making regarding therapy. Keywords: kidney cancer, sunitinib, predictive biomarker, target therapy, tyrosine kinase inhibitor
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".