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Record W2070483380 · doi:10.1517/13543784.2014.966900

Dovitinib: rationale, preclinical and early clinical data in urothelial carcinoma of the bladder

2014· review· en· W2070483380 on OpenAlexaff
C. Mazzola, Khurram Siddiqui, Michele Billia, Joseph L. Chin

Bibliographic record

VenueExpert Opinion on Investigational Drugs · 2014
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicFibroblast Growth Factor Research
Canadian institutionsSt Joseph's Health CentreWestern University
Fundersnot available
KeywordsReceptor tyrosine kinaseCancer researchTyrosine kinaseFibroblast growth factor receptor 3Tyrosine-kinase inhibitorFibroblast growth factor receptorIn vivoFibroblast growth factorMedicineIn vitroCancerReceptorBiologyInternal medicineBiotechnology

Abstract

fetched live from OpenAlex

INTRODUCTION: Bladder cancer (BC) is the third and fifth cancer in men in terms of incidence and mortality in the US. Overexpression and mutations of fibroblast growth factor receptor 3 (FGFR3) are frequently found in BC and can represent a very interesting therapeutic target. Different FGFR3-targeted strategies have been investigated through in vitro and in vivo settings, including FGFR3 tyrosine kinase inhibitors such as dovitinib . AREAS COVERED: The authors review the data that provide a scientific rationale for FGFR3-targeted therapy in BC. They also provide an evaluation of the currently available in vitro and in vivo data on the use of dovitinib in BC patients. EXPERT OPINION: The development and progression of BC rely on a very complex signaling network that involves many different receptors aside from FGFR3 and VEGFR2. The involved signaling network can also be very different from one BC to the other, and can also evolve through time in the same patient. Inhibiting only one single target may thus not be sufficient to achieve a complete downstream oncogenic signaling blockage. Additionally, in vitro data on the use of neutralizing monoclonal antibodies targeting FGFR3 show that it can be a more efficient strategy to reach the same goal, with the potential advantage of less toxicity.

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)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.794
Threshold uncertainty score0.935

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.161
GPT teacher head0.441
Teacher spread0.280 · 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 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

Citations20
Published2014
Admission routes1
Has abstractyes

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