A population-based overview of sequences of targeted therapy in metastatic renal cell carcinoma (mRCC).
Bibliographic record
Abstract
387 Background: There are several types of targeted therapy (TT) available to treat mRCC and data on outcomes and different sequences of therapies are required. Methods: Consecutive series of patients with mRCC treated with TT were examined. Multivariable analysis was performed when significant differences on univariable analysis were seen. Results: 2106 patients were included with a median follow-up of 36 months. 907 (43%) and 318 (15%) patients received subsequent second-line and third-line TT, respectively. Baseline characteristics of the groups below were not different except there were more patients with non-clear cell histology in the VEGF to mTOR group compared to the VEGF to VEGF group. When adjusting for the Heng et al poor risk criteria and non-clear cell histology, the hazard ratio of death for the VEGF to mTOR group vs the VEGF to VEGF group was 0.833 (95%CI 0.669-1.037, p=0.1016). When adjusting for poor risk criteria, the hazard ratio of death for the sunitinib to everolimus vs sunitinib to temsirolimus sequences was 0.774 (0.52-1.153, p=0.2086). Conclusions: The sequence of TT may not have a substantial effect on outcome but results of prospective randomized studies are awaited. [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 | 0.002 | 0.003 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".