Third- and Late Line Treatments of Metastatic Gastric Cancer: Still More to Be Done
Bibliographic record
Abstract
In recent years, advances of anticancer and supportive therapies have determined a gradual improvement in survival rates and patients' general conditions in metastatic gastric cancer (mGC), allowing them to receive further treatments. The choice of treatment is driven by performance status, age, stage of disease, number of metastatic sites and time from the first to third line of treatment. Targets such as microsatellite instability, PD-L1 expression, and HER2 overexpression or amplification may be addressed to personalise treatment and prolong survival. Despite a growing number of third line options that have provided clinicians with greater opportunities to customise treatments, up to date few agents have been demonstrated as effective after two standard lines for mGC; for these reasons, chemotherapy, immunotherapy, and targeted therapy were all widely investigated in both phase II and phase III studies. Overall, TAS-102, apatinib, regorafenib, nilotinib, trastuzumab, and pembrolizumab were demonstrated to be valid options in the third line scenario for mGC patient refractory to at least two lines of therapy. A multimodal approach based on chemotherapy, immunotherapy, targeted agents, a personalised nutritional programme as well as the research of new predictive biomarkers may pave the way to new strategies to identify the best treatment for each patient.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".