Systematic review of treatment options for gastric cancer and future therapeutic perspectives
Bibliographic record
Abstract
Background: Gastric cancer is the fourth most prevalent type of cancer and the second leading cause of cancer-related mortality worldwide, with an annual global incidence of 1 million cases and 700,000 deaths. Treatment modalities include surgery, chemotherapy, radiation therapy, and novel biological agents such as immune checkpoint inhibitors. The aim of the study is to summarise the existing literature on current treatment modalities and explore novel and emerging approaches to provide a detailed understanding of future advances in GC management. Methods: A systematic review was conducted from September 2022 to May 2024 using the online databases PubMed, Scopus, and Google Scholar. The risk of bias assessment was carried out using the Newcastle-Ottawa Scale. Results: The final review comprised 68 records. The analysis revealed that laparoscopic gastrectomy and other minimally invasive surgical approaches have yielded promising outcomes, either as standalone procedures or in combination with neoadjuvant and adjuvant chemotherapy regimens. The management of gastric cancer has been transformed by Human Epidermal Growth Factor Receptor 2-targeting agents, checkpoint inhibitors and other immunotherapies, with trastuzumab providing significant benefits when combined with chemotherapy. Conclusion: Larger prospective or randomized controlled trials should be conducted, incorporating neoadjuvant chemotherapy regimens, targeted agents, or other innovative approaches, to confirm current research findings and enhance the efficacy and safety of various therapeutic strategies. A thorough evaluation of existing treatments and novel therapeutic interventions is imperative to guide future research initiatives, formulate effective patient care strategies, and inform policy makers.
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 | 0.009 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".