Management of advanced kidney cancer: Kidney Cancer Research Network of Canada (KCRNC) consensus update 2021
Bibliographic record
Abstract
Advanced RCC has seen many treatment advances in the past several years, with the introduction of many novel therapies. Recent evidence from the KEYNOTE 426 and CheckMate 214 studies, and now the addition of the CheckMate 9ER and CLEAR studies, has mandated a rearrangement of treatment algorithms for advanced clear-cell RCC with the use of doublet immunotherapy or immunotherapy in combination VEGFR-TKI. We now await both clinical experience, prospective clinical trials, and development of predictive biomarkers to help inform the optimal choice and sequence of currently available treatment options. In absence of head-to-head comparison between combination approaches at this time, therapy should be individualized based on patient profiles and disease characteristics, and each agent chosen should be optimized to obtain the best results, with multidisciplinary care being paramount in achieving maximal benefit for patients. Ongoing participation in research and clinical trials to further our knowledge in this field continues to be an essential priority for healthcare professionals with an interest in advanced RCC.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 teacher head, 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".