Current status of minimally invasive surgery for gastric cancer: A literature review to highlight studies limits
Bibliographic record
Abstract
BACKGROUND: Gastric cancer represents a great challenge for health care providers and requires a multidisciplinary approach in which surgery plays the main role. Minimally invasive surgery has been progressively developed, first with the advent of laparoscopy and more recently with the spread of robotic surgery, but a number of issues are currently being investigate, including the limitations in performing effective extended lymph node dissections and, in this context, the real advantages of using robotic systems, the possible role for advanced Gastric Cancer, the reproducibility of completely intracorporeal techniques and the oncological results achievable during follow-up. METHOD: Searches of MEDLINE, Embase and Cochrane Central Register of Controlled Trials were performed to identify articles published until April 2014 which reported outcomes of surgical treatment for gastric cancer and that used minimally invasive surgical technology. Articles that deal with endoscopic technology were excluded. RESULTS: A total of 362 articles were evaluated. After the review process, data in 115 articles were analyzed. CONCLUSION: A multicenter study with a large number of patients is now needed to further investigate the safety and efficacy as well as long-term outcomes of robotic surgery, traditional laparoscopy and the open approach.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".