Gastrointestinal Stromal Tumors (GIST)—Paving the Way for Modern Oncology—Epidemiology, Diagnosis, Treatment
Bibliographic record
Abstract
Gastrointestinal stromal tumors (GIST) are relatively rare tumors arising in the gastrointestinal tract. Clinical presentations of GIST are related to the site of origin, tumor size, and presence of ulceration. Surgery followed by adjuvant treatment with the BCR-ABL tyrosine kinase inhibitor, imatinib, for 3 years, in high-risk tumors is the only curative modality. Neoadjuvant treatment with imatinib may be considered in the setting of locally advanced primary borderline resectable/unresectable disease. Treatment with imatinib in patients with metastatic or unresectable disease is associated with significant improvements in overall survival (OS) from 18 to 57 months. In patients with metastatic disease, those responding to imatinib therapy may be considered for surgery and this may be beneficial if resection of the primary and metastatic disease is feasible and if imatinib is continued post resection. Other locoregional treatments such as radiofrequency ablation (RFA) and hepatic artery embolization (HAE) with or without chemotherapy may achieve long-lasting disease control and may be considered in highly selected patients. In patients with primary or secondary resistance to imatinib, second-line treatment with sunitinib and third-line treatment with regorafenib is recommended. However, despite all of these advances, few patients with metastatic disease are cured and further trials of novel agents or combinations are required.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".