Renal Cell Carcinoma: Prognosis in the Era of Targeted Therapy
Bibliographic record
Abstract
Background: Significant changes in renal cell carcinoma (RCC) drug treatment and improved access to abdominal imaging have recently been implemented. The impact of these changes on patient characteristics and prognosis remains to be quantified. Methods: A population-based cohort of 210,418 RCC cases from the Centre for Cancer Registry Data (ZfKD) diagnosed in Germany between 2000 and 2019 was analyzed in this observational study. Three time periods of diagnosis were defined, the first (2000–2005) functioning as a control. The remaining were defined according to the introduction of tyrosine kinase targeting drugs (2006–2014) and checkpoint inhibitor drugs (2015–2019). Five-year relative survival (RS) trends for each risk group and metastatic RCC (mRCC) were determined using Poisson regression models. Results: Age at diagnosis and the proportion of low-risk disease increased, while the proportion of mRCC decreased (p < 0.0001). RS improved slightly between the first and last period in low (5-year RS 98.7% vs. 100.9%), intermediate (89.2% vs. 91.9%), and high-risk (76.6% vs. 80.3%), as well as mRCC (28.3% vs. 29.1%). The overall change in prognosis was significant in low (p = 0.0233) and high-risk groups (p = 0.0002), but not in intermediate-risk and mRCC groups. In a multivariate analysis, high-risk ccRCC patients appear to profit from drug treatment advances. Conclusions: Earlier detection has improved prognosis for the majority of RCC patients. Further efforts should be aimed at diagnosing more mRCC patients earlier, when surgical tumor removal remains feasible.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".