Metastatic gastric cancer – focus on targeted therapies
Bibliographic record
Abstract
Judith Meza-Junco, Michael B SawyerDepartment of Oncology, Cross Cancer Institute, Edmonton, Alberta, CanadaAbstract: Gastric cancer (GC) is currently the second leading cause of cancer death worldwide; unfortunately, most patients will present with locally advanced or metastatic disease. Despite recent progress in diagnosis, surgery, chemotherapy, and radiotherapy, prognosis remains poor. A better understanding of GC biology and signaling pathways is expected to improve GC therapy, and the integration of targeted therapies has recently become possible and appears to be promising. This article focuses on anti-Her-2 therapy, specifically trastuzumab, as well as other epidermal growth factor receptor antagonists such as cetuximab, panitumub, matuzumab, nimotzumab, gefitinib, and erlotinib. Additionally, drugs that target angiogenesis pathways are also under investigation, particulary bevacizumab, ramucirumab, sorafenib, sunitinib, and cediranib. Other targeted agents in preclinical or early clinical development include mTOR inhibitors, anti c-MET, polo-like kinase 1 inhibitors, anti-insulin-like growth factor, anti-heat shock proteins, and small molecules targeting Hedgehog signaling.Keywords: gastric cancer, targeted therapy, antiangiogenesis drugs, anti-EGFR drugs
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".