Current management of gastric adenocarcinoma: a narrative review
Bibliographic record
Abstract
Gastric adenocarcinoma is a leading cause of cancer death worldwide. The management of this aggressive malignancy largely depends on tumor characteristics especially stage. Superficial early-stage gastric cancer can be safely managed by endoscopic resection, though clear negative deep and lateral margins must be obtained. Optimal surgical resection is an essential part of the treatment for locally advanced gastric adenocarcinoma, with perioperative and adjuvant therapies having significant impact on long-term outcomes. Chemoradiation is reserved for patients with suboptimal surgical resection. Recent therapeutic advances have prolonged survival in patients with metastatic gastric adenocarcinoma, include checkpoint inhibitors and biomarker-directed therapy. Targeted therapies in gastric adenocarcinoma include monoclonal antibodies directed against vascular endothelial growth factor (VEGF), vascular endothelial growth factor receptor-2 (VEGFR-2), and human epidermal growth factor receptor 2 (HER2). While anti-VEGF therapies were not found beneficial in the perioperative setting, the effectiveness of HER2 targeted agents in resectable HER2-positive gastric adenocarcinoma is being studied. Microsatellite instability (MSI) varies greatly in patients with gastric adenocarcinoma between 5-20% based on ethnic origin, tumour heterogeneity and staging. The role chemotherapy in the perioperative setting for patients with MSI-high tumors remains controversial while immunotherapy demonstrates promising results in preliminary studies. Immune checkpoint inhibitors in combination with chemotherapy has been shown to improve outcomes in patients with metastatic gastric adenocarcinoma who express programmed cell death 1 ligand 1 (PD-L1) and is now being investigated in the perioperative setting.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".