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Record W4303627977 · doi:10.3390/curroncol29100573

Current Clinical Practice of Precision Medicine Using Comprehensive Genomic Profiling Tests in Biliary Tract Cancer in Japan

2022· review· en· W4303627977 on OpenAlexvenueno aff
Masashi Kanai

Bibliographic record

VenueCurrent Oncology · 2022
Typereview
Languageen
FieldMedicine
TopicCholangiocarcinoma and Gallbladder Cancer Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineBiliary tract cancerProfiling (computer programming)Precision medicineClinical PracticeBiliary tractCancer MedicineBioinformaticsCancerComputational biologyInternal medicineMedical physicsPathologyFamily medicineBiology

Abstract

fetched live from OpenAlex

With the recent advances of next generation sequencing technologies, comprehensive genomic profiling (CGP) tests, which are designed to measure more than hundreds of cancer-related genes at a time, have now been widely introduced into daily clinical practice. For the patients whose tumor samples are not fit for tissue-based CGP tests, a blood-based CGP test (liquid biopsy) is available as an alternative option. Three CGP tests, “OncoGuide NCC™Oncopanel System (124 genes)”, “FoundationOne®CDx (324 genes)”, and “Founda-tionOne®CDx Liquid (324 genes)”, are now reimbursed by public insurance in 233 hospitals designated for cancer genomic medicine in Japan. In biliary tract cancer, the prevalence of druggable variants is relatively higher compared to other cancer types and the European Society for Medical Oncology recommends routine use of CGP tests for advanced biliary tract cancer to guide treatment options. The latest National Cancer Center Network guideline lists eight druggable markers (NTRK fusion, MSI-H, TMB-H, BRAF V600E, FGFR2 fusions/rearrangement, IDH1 mutations, RET fusion, and HER2 overexpression) and matched therapies. In Japan, matched therapies for four markers (NTRK, MSI-H, TMB-H, and FGFR2) are reimbursed by public insurance (as of September 2022). The progress of genomic profiling technology will contribute to the improvement of the dismal clinical outcomes of this disease in the future.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.948
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.003
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 head0.456
GPT teacher head0.575
Teacher spread0.119 · 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.

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

Citations12
Published2022
Admission routes1
Has abstractyes

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