Real-World Efficacy and Toxicity of Ipilimumab and Nivolumab as First-Line Treatment of Metastatic Renal Cell Carcinoma (mRCC) in a Subpopulation of Elderly and Poor Performance Status Patients
Bibliographic record
Abstract
BACKGROUND: Ipilimumab and nivolumab (ipi/nivo) improved overall survival (OS) compared to sunitinib in the pivotal Checkmate 214 trial of metastatic renal cell carcinoma (mRCC) with International Metastatic RCC Database Consortium (IMDC) intermediate/poor risk disease. We evaluated the efficacy and toxicity of ipi/nivo in older and frailer populations in a real-world mRCC cohort. METHODS: Analysis was conducted on a real-world cohort with mRCC (N = 551) treated with first-line ipi/nivo from the Canadian Kidney Cancer information system (CKCis) database from January 2014 to December 2021. A comparison was made between outcomes and toxicity in patients 1. <70 versus (vs.) ≥70 yo, 2. <75 vs. ≥75 yo, and 3. KPS ≥70 vs. <70 yo. OS, progression-free survival (PFS), and time to treatment failure (TTF) were calculated by Kaplan-Meier analysis. Log-rank tests were used for comparison between groups. RESULTS: -value < 0.0001). CONCLUSIONS: The use of ipi/nivo in mRCC demonstrated similar survival outcomes and toxicity in an older patient population. In patients with a poor performance status, it was associated with inferior OS and PFS. We believe that ipi/nivo is a reasonable treatment option for these patient populations, particularly in older 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".