Fourth-line targeted therapy in metastatic renal cell carcinoma (mRCC): Results from the International mRCC Database Consortium (IMDC).
Bibliographic record
Abstract
498 Background: Fourthline targeted therapy efficacy in mRCC is not well characterized and is not reimbursed in many jurisdictions worldwide. Methods: The IMDC consists of consecutive patient series from 35 international cancer centers. It was queried for mRCC patients who received fourth line targeted therapy. Kaplan Meier estimates were used for time to treatment failure (TTF) and overall survival (OS). Results: 594 out of 7498 (8%) mRCC patients initially treated with first line targeted therapy eventually received fourth line therapy from a class of approved agents. Baseline characteristics are displayed in Table 1. The most common fourth line therapies were everolimus 17%, sorafenib 15%, axitinib 13%, pazopanib 13%, sunitinib 13%, nivolumab 7%. IMDC prognostic group distributions (Heng et al JCO 2009) and their associated survivals (both determined from fourth line therapy initiation) were 5% favorable risk (OS 23.1 (14.7-not reached)), 66% intermediate risk (OS 13.8 (11.4-17.5)), and 29% poor risk (OS 7.8 (4.93-12.2)) (OS p<0.0001). Overall response rate for fourth-line therapy was 12.5% and 41.5% had stable disease in those patients that were evaluable (n=407). Median TTF on fourth line therapy was 4.40 months (95% CI 3.98-5.06) and median OS from fourth line therapy initiation was 12.8 months (95% CI 11.4-14.4). Conclusions: Fourth line targeted therapy has demonstrated activity, is uncommon, and should be offered to clinically eligible patients. Further studies are required to determine appropriate sequencing. IMDC criteria appear to stratify favorable/intermediate/poor risk patients well in the fourth line setting. [Table: see text]
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".