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Record W4410207760 · doi:10.1093/oncolo/oyae294

Efficacy of 2 different fibroblast growth factor receptor-inhibitors in a patient with extrahepatic cholangiocarcinoma harboring an FGFR2 mutation: a case report

2025· article· en· W4410207760 on OpenAlexaff
Ilianna Galli-Vareia, Petr Szturz, Ioannis A. Voutsadakis, Nicolas Villard, Georgia Tsoumakidou, Mapi Fleury, Gabriela Herrera, François Fasquelle, Sébastien Godat, Antonia Digklia

Bibliographic record

VenueThe Oncologist · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicFibroblast Growth Factor Research
Canadian institutionsNOSM University
Fundersnot available
KeywordsFibroblast growth factor receptorFibroblast growth factor receptor 2Cancer researchFibroblast growth factor receptor 3Point mutationMutationTyrosine kinaseReceptor tyrosine kinaseFibroblast growth factor receptor 4Fibroblast growth factorFGF19Receptor Protein-Tyrosine KinasesReceptorGrowth factor receptorCancerMedicineBiologyInternal medicineGeneGenetics

Abstract

fetched live from OpenAlex

Cholangiocarcinoma (CCA) is a type of cancer with few effective systemic therapies. Elucidation of the molecular landscape of the disease from genomic studies based on next-generation sequencing (NGS) has contributed to the introduction of new targeted therapies. One of these treatments consists of a class of small molecules that target members of the fibroblast growth factor receptors (FGFRs) family of receptor tyrosine kinases. We report here on a patient with a cholangiocarcinoma bearing an FGFR2 mutation. The patient was treated with 2 different FGFR inhibitors, as the first caused ocular toxicity. She obtained clinical benefits from both. This case illustrates the efficacy of FGFR inhibitors on cholangiocarcinoma with specific point mutations.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0040.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.287
Teacher spread0.273 · 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 designCase report
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

Citations2
Published2025
Admission routes1
Has abstractyes

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