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Record W1976171988 · doi:10.5539/cco.v1n1p1

Practical Insights and Challenges in the Rational Use of Targeted Agents in Metastatic Clear Cell Renal Carcinoma

2012· article· en· W1976171988 on OpenAlexvenueno aff
Swati Andhavarapu, Winston Tan

Bibliographic record

VenueCancer and Clinical Oncology · 2012
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsnot available
Fundersnot available
KeywordsRenal cell carcinomaMedicineTargeted therapyImmunotherapyOncologyInternal medicineCarcinomaDiseaseCancer researchCancer

Abstract

fetched live from OpenAlex

Renal cell carcinoma (RCC) accounts for approximately 4% of all primary cancers diagnosed in the United States with an estimated 13,000 deaths in 2010. Metastatic disease is the initial presentation in approximately 30% of the patients. Until 2006, immunotherapy with Interferon-? and Interleukin-2 represented the primary treatment of advanced RCC but better understanding of the pathogenesis and molecular biology of RCC paved the way for targeted molecular therapies. Six molecular targeted agents have been approved for the treatment of metastatic renal cell carcinoma (mRCC). This review summarizes the approved targeted agents, their toxicities and practical insights into the treatment of mRCC.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.238

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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.

Opus teacher head0.413
GPT teacher head0.462
Teacher spread0.049 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations0
Published2012
Admission routes1
Has abstractyes

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