Application of IMDC criteria across first-line (1L) and second-line (2L) therapies in metastatic renal-cell carcinoma (mRCC): New and updated benchmarks of clinical outcomes.
Bibliographic record
Abstract
5063 Background: In patients with mRCC, the International mRCC Database Consortium (IMDC) criteria have been validated as a prognostic tool in patients treated with targeted therapy in the 1-4L settings and with 2L Nivolumab (Nivo). However, it is unknown whether the IMDC criteria can be used to risk stratify in recently approved 1L IO combination therapies, including Ipilimumab + Nivolumab (IOIO) and Axitinib + Pembrolizumab/Avelumab (IOVE). We sought to assess the ability of the IMDC criteria to risk stratify with the use of novel 1L IO combinations and provide updated benchmarks for older 1L and 2L treatments. Methods: Patients with mRCC starting systemic therapy between 2010-2019 were identified through the IMDC. IMDC risk score was calculated at the time of starting the line of therapy of interest. The primary endpoint was overall survival (OS) from time of initiating the treatment of interest. Results: From a total of 6596 unique patients, 5043 treated in the 1L setting and 2498 treated in the 2L setting were included in the analysis. Across the entire cohort, median age was 61, 73% were male, 16% had sarcomatoid features, 79% underwent nephrectomy and 88% had clear-cell histology. IMDC risk groups for 1L and 2L treatment were 17%, 57%, 27% and 10%, 60%, 30% for favourable-, intermediate- and poor-risk disease, respectively. IMDC criteria appropriately risk stratified into 3 prognostic groups in 1L IOIO and 1L IOVE combinations, in addition to older treatments: 1L VEGF TT, 2L VEGF TT, 2L Nivo and 2L Everolimus. Results are displayed in Table. Due to the novelty of 1L IO combinations, median follow up time was shorter and thus landmark OS values are presented. Conclusions: IMDC criteria may be used to risk stratify in recently approved 1L IO combination therapies in addition to older 1L and 2L treatments. These data provide contemporary benchmarks for OS that may be used for patient counseling and trial design. [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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".