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Record W4386121517 · doi:10.1155/2023/4248128

Gastrointestinal Stromal Tumours (GISTs) with KRAS Mutation: A Rare but Important Subset of GISTs

2023· article· en· W4386121517 on OpenAlexaff
Dorinda Mullen, Rajkumar Vajpeyi, José‐Mario Capo‐Chichi, Newton A C S Wong, Runjan Chetty

Bibliographic record

VenueCase Reports in Gastrointestinal Medicine · 2023
Typearticle
Languageen
FieldMedicine
TopicGastrointestinal Tumor Research and Treatment
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsKRASGiSTPDGFRAMedicineCancer researchGastrointestinal tractImatinibImatinib mesylateMutationImmunohistochemistryStomachPathologyStromal cellInterstitial cell of CajalInternal medicineCancerBiologyColorectal cancerGeneGenetics

Abstract

fetched live from OpenAlex

Gastrointestinal stromal tumours (GISTs) are the most common mesenchymal tumours of the GI tract, usually found in the stomach, jejunum, and ileum. Typically, they are KIT or PDGFR-mutated, allowing for targetable treatments with tyrosine kinase inhibitors such as imatinib. Here, we present two KRAS-mutated wild-type gastrointestinal tumours (GISTs). Both cases occurred in the small bowel of females. Immunohistochemical studies on both tumours showed KIT and DOG-1 positivity, with SDHB retained. Molecular analysis revealed a KRAS G12D mutation and a KRAS G13D mutation, respectively. Wild-type GISTs are extremely uncommon. They typically occur in the stomach or the small bowel. KRAS is one of the genes implicated in this subset of GIST, with KRAS G12D being the most frequently encountered mutation. GIST KRAS mutations can arise alone or in conjunction with KIT, PDFRA, or BRAF mutations. Identification of these rare molecular subtypes is clinically important due to their resistance to imatinib therapy.

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: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.030
GPT teacher head0.313
Teacher spread0.282 · 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

Citations7
Published2023
Admission routes1
Has abstractyes

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