The very favorable metastatic renal cell carcinoma (mRCC) risk group: Data from the International Metastatic RCC Database Consortium (IMDC).
Bibliographic record
Abstract
339 Background: The IMDC criteria have been used as a prognostic tool for patients with mRCC receiving single agent VEGF-targeted drugs, and more recently combination immuno-oncology (IO) +/- VEGF-targeted agents, which improve outcomes over VEGF TKI monotherapy. We sought to identify a subset of patients with very favorable outcomes, for which less intensive therapy might be considered. Methods: Utilizing the IMDC dataset, 1638 patients with IMDC favorable risk disease received first-line systemic therapy. Patients were randomly selected in a 2:1 ratio to the training and testing sets, stratified by year of systemic therapy initiation. Multivariable Cox regression estimated prognostic factors for overall survival (OS). Results: Median age was 63 (range 21-95) years and 98% had received prior nephrectomy. First-line systemic therapy consisted of targeted therapy (91%), IO-combination regimens (8%), or other (1%). From the training data, three variables (primary diagnosis to systemic therapy <3 vs ≥3yr; Karnofsky Performance Status 80 vs >80; presence of brain, liver, or bone metastasis) significantly predicted for OS in the multivariable model (hazard ratio 1.4~1.5, p-values<0.05). The model had similar performance in the test dataset (C-index=0.64). Using the 3 included risk factors, patients were classified to very favorable risk (0 risk factors, 29% of patients) or favorable risk disease (≥1 risk factors, 71% of patients). Clinical outcomes for the two risk groups are presented in the table below. Conclusions: We identified a very favorable risk group in the IMDC criteria in RCC patients treated with first-line therapy. External validation including populations receiving IO containing therapies is ongoing. [Table: see text]
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".