A comparison of combined immune checkpoint inhibitors (IO) versus vascular endothelial growth factor receptor (VEGFR) tyrosine kinase inhibitors (TKI) in the treatment of advanced clear cell renal cell carcinoma.
Bibliographic record
Abstract
692 Background: IO and VEGFR/TKI are approved treatment options, either alone or combination, in advanced clear cell RCC. Currently, there is a lack of evidence comparing efficacy and safety outcomes amongst these therapies. We sought to compare the published data for the various options with respect to efficacy and safety. Methods: A literature search using PubMed, clinicaltrials.gov, ASCO and ESMO meeting abstract databases from January 1, 2015 to June 30, 2019 to identify eligible clinical trials in advanced clear cell RCC involving at least one immunotherapy agent was performed. Due to small sample sizes in the various cohorts, descriptive statistics were provided. Weighting of estimates was based on sample size of the intervention arms. Results: 14 studies involving 6,197 pts were identified. The median age was 62 years (54.8, 64), men constituting median of 75%, and prior TKI receipt in 63%. There were 7 studies in each treatment arm. The efficacy outcomes did not demonstrate statistical differences. In the safety analyses, IO + VEGFR/TKI demonstrated the highest serious adverse event rate, correlating with treatment discontinuation rates. Conclusions: IO + IO and IO + VEGFR/TKI showed comparable efficacy and toxicity outcomes in the treatment of advanced clear cell RCC. There is a non-significant trend towards increased efficacy in some outcomes with IO + VEGFR/TKI, with possibly increased adverse events. Further studies with patient level data, cross-comparative trials, and predictive biomarkers are needed to establish a therapeutic matrix for RCC pts.[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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".