Management of Advanced Kidney Cancer: Kidney Cancer Research Network of Canada (KCRNC) consensus update 2019
Bibliographic record
Abstract
Advanced RCC has seen many treatment advances in the last several years, with the introduction of many novel therapies. Recent evidence from the KEYNOTE 426 and CheckMate 214 studies has mandated a rearrangement of treatment algorithms for advanced clear-cell RCC. We now await both clinical experience and prospective clinical trials to help inform the optimal sequence of therapy with these newer therapies, VEGF-targeted therapies and other evidence-based options. 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. Therapy should be individualized based on patient profiles and disease characteristics, and each agent chosen should be optimized to obtain best results, with multidisciplinary care being paramount in achieving maximal benefit for patients.
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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".