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Record W2495051483 · doi:10.17925/ohr.2014.10.2.110

Systemic Treatment for Gastrointestinal Stromal Tumor—A State of Art

2014· article· en· W2495051483 on OpenAlexaff
Xiaolan Feng, Don Morris

Bibliographic record

VenuetouchREVIEWS in Oncology & Haematology · 2014
Typearticle
Languageen
FieldMedicine
TopicGastrointestinal Tumor Research and Treatment
Canadian institutionsAlberta Health Services
Fundersnot available
KeywordsGiSTRegorafenibSunitinibMedicineImatinibStromal tumorTyrosine-kinase inhibitorImatinib mesylateOncologyTargeted therapyTyrosine kinaseInternal medicineAdjuvantCancer researchStromal cellCancerReceptorColorectal cancer

Abstract

fetched live from OpenAlex

The availability of the tyrosine kinase inhibitor (TKI) small molecule imatinib has revolutionized the systemic treatment for gastrointestinal stromal tumor (GIST), historically one of the most chemoresistant solid malignancies. Prior to imatinib availability approximately 14 years ago, surgery was the only effective treatment modality. Imatinib is now accepted as the first-line systemic treatment for advanced GIST and subsequently has become the standard systemic treatment for GIST in the neoadjuvant and adjuvant settings. Sunitinib and regorafenib have been approved for second- and third-line treatments, respectively, for patients with advanced GIST progressing on imatinib. The dramatic and continuing efficacy of TKIs targeting oncogenic driver pathways such as KIT, platelet-derived growth factor receptor alpha (PDGFR〈), and vascular epithelial growth factor receptors (VEGFs), in advanced GIST supports the utility of targeted therapy in oncogene addicted solid malignancies. Molecular mutational diagnostics has further defined subpopulations of responders. Although significant gains have been made in the treatment of GIST patients, future research is still warranted to help further improve clinical outcomes of patients with GIST.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.633
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.047
GPT teacher head0.373
Teacher spread0.326 · 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 designObservational
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
Published2014
Admission routes1
Has abstractyes

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