Patterns of care among patients receiving sequential targeted therapies for advanced renal cell carcinoma: A retrospective chart review in the <scp>USA</scp>
Bibliographic record
Abstract
Objectives To assess real‐world treatment patterns of targeted therapies after failure of first‐line tyrosine kinase inhibitors in patients with advanced renal cell carcinoma. Methods A large, retrospective review of medical charts of patients with advanced renal cell carcinoma in the USA was carried out. Descriptive statistics were used to summarize physicians’ and patients’ characteristics, treatment sequences, and reasons for treatment choices. P‐values were calculated using χ2‐tests for categorical variables and Wilcoxon rank‐sum tests for continuous variables. A descriptive comparison was carried out between current results and those of a previous treatment pattern study conducted in 2012 to identify changes in treatment patterns over time. Results Sunitinib and everolimus remained the most commonly‐used first and second targeted therapies, respectively. Among patients who continued to a third targeted therapy, everolimus and axitinib were the most commonly‐used treatments after second targeted therapy with a tyrosine kinase inhibitor and a mammalian target of rapamycin inhibitor, respectively. The use of pazopanib as first targeted therapy, and of axitinib and sorafenib as second targeted therapies, increased over time. Efficacy, treatment guidelines and a different mechanism of action were the main reasons given by physicians for choosing among second targeted therapies after failure of a first tyrosine kinase inhibitor. Conclusions The results of the present study document patterns of care during a period of rapid and ongoing therapeutic advancement in advanced renal cell carcinoma. Sequencing of therapies warrants ongoing analysis in light of new agents entering the advanced renal cell carcinoma treatment landscape.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".