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Record W2966762477 · doi:10.21037/tgh.2019.07.02

Genetics of gastric cancer: what do we know about the genetic risks?

2019· review· en· W2966762477 on OpenAlexafffund
Thomas P. Slavin, Jeffrey N. Weitzel, Susan L. Neuhausen, Kasmintan A. Schrader, Carla Oliveíra, Rachid Karam

Bibliographic record

VenueTranslational Gastroenterology and Hepatology · 2019
Typereview
Languageen
FieldMedicine
TopicGenetic factors in colorectal cancer
Canadian institutionsUniversity of British Columbia
FundersEuropean Regional Development FundFundação para a Ciência e a TecnologiaCanadian Institutes of Health ResearchMinistério da Ciência, Tecnologia e InovaçãoNational Cancer InstituteNational Institutes of HealthMichael Smith Health Research BCOxnard FoundationStop Cancer
KeywordsCancerMedicineGermlineGenetic predispositionDiseaseFamily historyGenetic testingGermline mutationGeneticsBioinformaticsGeneOncologyInternal medicineMutationBiology

Abstract

fetched live from OpenAlex

An appreciable number of patients with gastric cancer have an underlying hereditary cancer susceptibility syndrome as the cause of their gastric cancer, particularly those with early onset gastric cancer or a family history of gastric or other cancers. Pathogenic germline variants in specific genes account for the known gastric cancer predisposition syndromes. Germline genetic testing can identify individuals and their family members who carry inherited pathogenic gene variants, and thus have increased risk of developing gastric or other cancers. Ideally, germline pathogenic variants can be identified in family members before the onset of disease, when early detection or prevention strategies can be implemented most effectively to decrease gastric cancer- related morbidity and mortality. This article reviews some of the currently known gastric cancer predisposition syndromes and their associated cancer risks. We also discuss current research and advances in the field of genetic gastric cancer susceptibility.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.959
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.073
GPT teacher head0.360
Teacher spread0.286 · 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

Citations56
Published2019
Admission routes2
Has abstractyes

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