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Record W2028238072 · doi:10.1111/bcpt.12365

The Adverse Effects of Sorafenib in Patients with Advanced Cancers

2014· review· en· W2028238072 on OpenAlexafffund
Ye Li, Zu‐Hua Gao, Xian‐Jun Qu

Bibliographic record

VenueBasic & Clinical Pharmacology & Toxicology · 2014
Typereview
Languageen
FieldMedicine
TopicThyroid Cancer Diagnosis and Treatment
Canadian institutionsMcGill University
FundersMcGill University Health CentreCapital Medical UniversityNational Natural Science Foundation of ChinaMcGill University
KeywordsSorafenibMedicineAdverse effectOncologyInternal medicineThyroid cancerCancerAngiogenesisTyrosine-kinase inhibitorPharmacologyHepatocellular carcinoma

Abstract

fetched live from OpenAlex

Sorafenib is the first multi-kinase inhibitor (TKI) approved for the treatment of advanced hepatocellular cancer (HCC) and metastatic renal cell cancer (RCC) and is increasingly being used to treat patients with well-differentiated radioiodine-resistant thyroid cancer (DTC). Sorafenib demonstrates targeted activity on several families of receptor and non-receptor tyrosine kinases that are involved in angiogenesis, tumour growth and metastatic progression of cancer. Sorafenib treatment results in long-term efficacy and low incidence of life-threatening toxicities. Although sorafenib has demonstrated many benefits in patients, the adverse effects cannot be ignored. The most common treatment-related toxicities include diarrhoea, fatigue, hand-foot skin reaction and hypertension. Most of these toxicities are considered mild to moderate and manageable to varying degrees; however, cardiovascular events might lead to death. In this MiniReview, we summarize the adverse effects of sorafenib that commonly occur in patients with advanced cancers.

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)0.000
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: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.025
GPT teacher head0.409
Teacher spread0.384 · 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 designSystematic review
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

Citations167
Published2014
Admission routes2
Has abstractyes

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