First-, second-, third-line therapy for mRCC: benchmarks for trial design from the IMDC
Bibliographic record
Abstract
BACKGROUND: Limited data exist on outcomes for metastatic renal cell carcinoma (mRCC) patients treated with multiple lines of therapy. Benchmarks for survival are required for patient counselling and clinical trial design. METHODS: Outcomes of mRCC patients from the International mRCC Database Consortium database treated with 1, 2, or 3+ lines of targeted therapy (TT) were compared by proportional hazards regression. Overall survival (OS) and progression-free survival (PFS) were calculated using different population inclusion criteria. RESULTS: In total, 2705 patients were treated with TT of which 57% received only first-line TT, 27% received two lines of TT, and 16% received 3+ lines of TT. Overall survival of patients who received 1, 2, or 3+ lines of TT were 14.9, 21.0, and 39.2 months, respectively, from first-line TT (P<0.0001). On multivariable analysis, 2 lines and 3+ lines of therapy were each associated with better OS (HR=0.738 and 0.626, P<0.0001). Survival outcomes for the subgroups were as follows: for all patients, OS 20.9 months and PFS 7.2 months; for those similar to eligible patients in the first-line ADAPT trial, OS 14.7 months and PFS 5.6 months; for those similar to patients in first-line TIVO-1 trial, OS 24.8 months and PFS 8.2 months; for those similar to patients in second-line INTORSECT trial, OS 13.0 months and PFS 3.9 months; and for those similar to patients in the third-line GOLD trial, OS 18.0 months and PFS 4.4 months. CONCLUSIONS: Patients who are able to receive more lines of TT live longer. Survival benchmarks provide context and perspective when interpreting and designing clinical trials.
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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 | 0.332 | 0.358 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".