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Record W2032490154 · doi:10.2174/157488406778249325

Sorafenib (BAY 43-9006): Review of Clinical Development

2006· review· en· W2032490154 on OpenAlexaff
Raymond Ng, Eric X. Chen

Bibliographic record

VenueCurrent Clinical Pharmacology · 2006
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMelanoma and MAPK Pathways
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsSorafenibMedicineHepatocellular carcinomaRashPharmacologyTyrosine-kinase inhibitorRenal cell carcinomaInternal medicinePlatelet-derived growth factor receptorOncologyToxicityCancerReceptor

Abstract

fetched live from OpenAlex

Sorafenib (BAY 43-9006) is a novel oral bis-aryl urea compound originally developed as an inhibitor to RAF kinase for its anti-proliferative property. It also inhibits receptor tyrosine kinases of multiple pro-angiogenic factors such as VEGFR-2/3, Flt-3/ and PDGFR-beta. The combination of both its anti-proliferative and anti-angiogenic properties makes sorafenib an attractive agent in cancer treatment. Phase I studies demonstrated that sorafenib was well tolerated, and the recommended phase II dose was 400 mg twice daily continuously. Common toxicities included skin toxicity (rash and hand-foot syndrome), gastrointestinal toxicities (nausea and diarrhea) and fatigue. Anti-tumor activities were observed in multiple tumors types including renal cell carcinoma and hepatocellular carcinoma. Randomized phase III studies in these tumor types are ongoing, and results are eagerly waited.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.004

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 head0.248
GPT teacher head0.545
Teacher spread0.297 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations31
Published2006
Admission routes1
Has abstractyes

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