Metastatic non–clear cell renal cell carcinoma treated with targeted therapy agents: Characterization of survival outcome and application of the International mRCC Database Consortium criteria
Bibliographic record
Abstract
BACKGROUND: This study aimed to apply the International mRCC Database Consortium (IMDC) prognostic model in metastatic non-clear cell renal cell carcinoma (nccRCC). In addition, the survival outcome of metastatic nccRCC patients was characterized. METHODS: Data on 2215 patients (1963 with clear-cell RCC [ccRCC] and 252 with nccRCC) treated with first-line VEGF- and mTOR-targeted therapies were collected from the IMDC. Time to treatment failure (TTF) and overall survival (OS) were compared in groups with favorable, intermediate, and poor prognoses according to IMDC prognostic criteria RESULTS: The median OS of the entire cohort was 20.9 months. nccRCC patients were younger (P < .0001) and more often presented with low hemoglobin (P = .014) and elevated neutrophils (P = .0001), but otherwise had clinicopathological features similar to those of ccRCC patients. OS (12.8 vs 22.3 months; P < .0001) and TTF (4.2 vs 7.8 months; P < .0001) were worse in nccRCC patients compared with ccRCC patients. The hazard ratio for death and TTF when adjusted for the prognostic factors was 1.41 (95% CI, 1.19-1.67; P < .0001) and 1.54 (95% CI, 1.33-1.79; P < .0001), respectively. The IMDC prognostic model reliably discriminated 3 risk groups to predict OS and TTF in nccRCC; the median OS of the favorable, intermediate, and poor prognosis groups was 31.4, 16.1, and 5.1 months, respectively (P < .0001), and the median TTF was 9.6, 4.9, and 2.1 months, respectively (P < .0001). CONCLUSIONS: Although targeted agents have significantly improved the outcome of patients with nccRCC, for the majority survival is still inferior compared with patients with ccRCC. The IMDC prognostic model reliably predicts OS and TTF in nccRCC and ccRCC patients.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".