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Record W2465616243 · doi:10.4081/oncol.2016.295

Contemporary treatment of metastatic renal cell carcinoma

2016· review· en· W2465616243 on OpenAlexaff
Igor Stukalin, Nimira Alimohamed, Daniel Yick Chin Heng

Bibliographic record

VenueOncology Reviews · 2016
Typereview
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsFoothills Medical Centre
Fundersnot available
KeywordsCabozantinibMedicineRenal cell carcinomaNivolumabTargeted therapyClinical trialSunitinibOncologyAxitinibKidney cancerPI3K/AKT/mTOR pathwayImmunotherapyTemsirolimusInternal medicineCancer researchDiscovery and development of mTOR inhibitorsCancerSignal transduction

Abstract

fetched live from OpenAlex

The introduction of targeted therapy has revolutionized the treatment of patients with metastatic renal cell carcinoma (mRCC). The current standard of care focuses on the inhibition of angiogenesis through the targeting of the vascular endothelial growth factor receptor (VEGFR) and the mammalian target of rapamycin (mTOR). Over the past few years, research exploring novel targeted agents has blossomed, leading to the approval of various targeted therapies. Furthermore, results from the CheckMate025 and the METEOR trials have brought about two additional novel options: the programmed cell death 1 (PD-1) checkpoint inhibitor nivolumab and the MET/VEGFR/AXL inhibitor cabozantinib, respectively. With the variety of therapeutic agents available for treatment of mRCC, research examining appropriate sequencing and combinations of the drugs is ongoing. This review discusses the role of prognostic criteria, such as those from the International Metastatic Renal Cell Carcinoma Database Consortium (IMDC) criteria. It also covers the current standard of treatment for mRCC with targeted therapy in first-, second-, and third-line setting. Additionally, the novel mechanism of action of nivolumab and cabozantinib, therapeutic sequencing and ongoing clinical trials are discussed.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.953
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0110.003
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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.

Opus teacher head0.227
GPT teacher head0.413
Teacher spread0.185 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations41
Published2016
Admission routes1
Has abstractyes

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