Systemic Treatment for Gastrointestinal Stromal Tumor—A State of Art
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".