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Record W2113562909 · doi:10.1177/1758834009352498

Review: Targeted therapy for metastatic renal cell carcinoma: current treatment and future directions

2009· article· en· W2113562909 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueTherapeutic Advances in Medical Oncology · 2009
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsUniversity of CalgaryBC Cancer Agency
Fundersnot available
KeywordsTemsirolimusPazopanibMedicineAxitinibSorafenibSunitinibBevacizumabEverolimusRenal cell carcinomaTargeted therapyOncologyPI3K/AKT/mTOR pathwayAdjuvantClinical trialVascular endothelial growth factorAdjuvant therapyInternal medicinePharmacologyDiscovery and development of mTOR inhibitorsCancerVEGF receptorsChemotherapy

Abstract

fetched live from OpenAlex

An understanding of vascular endothelial growth factor (VEGF) and mammalian target of rapamycin (mTOR) pathways has greatly changed the way metastatic renal cell carcinoma (RCC) is treated. Based on available phase III randomized trials, anti-VEGF agents such as sunitinib, sorafenib, bevacizumab-based therapy, and mTOR-targeted agents such as temsirolimus and everolimus have been used in the treatment armamentarium for this disease. Now that agents directed against these pathways have largely replaced immunotherapy as the standard of care, new questions have emerged and are the subject of ongoing clinical trials. The development of new targeted therapies including axitinib, pazopanib, cediranib, volociximab, tivozanib (AV-951), BAY 73-4506, and c-met inhibitors such as GSK1363089 and ARQ197 may potentially expand the list of treatment options. Sequential and combination targeted therapies are currently under investigation in advanced disease as are adjuvant and neo-adjuvant approaches around nephrectomy.

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.

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)0.000
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.996
Threshold uncertainty score0.847

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.035
GPT teacher head0.381
Teacher spread0.346 · 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