Bibliographic record
Abstract
Gastric cancer remains a significant health problem worldwide and surgery is currently the only potentially curative treatment option. Gastric cancer surgery is generally considered to be high risk surgery and five-year survival rates are poor, therefore a continuous strive to improve outcomes for these patients is warranted. Fortunately, in the last decades several potential advances have been introduced that intervene at various stages of the treatment process. This review provides an overview of methods implemented in pre-, intra- and postoperative stage of gastric cancer surgery to improve outcome. Better preoperative risk assessment using comorbidity index (e.g., Charlson comorbidity index), assessment of nutritional status (e.g., short nutritional assessment questionnaire, nutritional risk screening - 2002) and frailty assessment (Groningen frailty indicator, Edmonton frail scale, Hopkins frailty) was introduced. Also preoperative optimization of patients using prehabilitation has future potential. Implementation of fast-track or enhanced recovery after surgery programs is showing promising results, although future studies have to determine what the exact optimal strategy is. Introduction of laparoscopic surgery has shown improvement of results as well as optimization of lymph node dissection. Hyperthermic intraperitoneal chemotherapy has not shown to be beneficial in peritoneal metastatic disease thus far. Advances in postoperative care include optimal timing of oral diet, which has been shown to reduce hospital stay. In general, hospital volume, i.e., centralization, and clinical audits might further improve the outcome in gastric cancer surgery. In conclusion, progress has been made in improving the surgical treatment of gastric cancer. However, gastric cancer treatment is high risk surgery and many areas for future research remain.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".