Robot-assisted laparoscopic gastrectomy for gastric cancer
Bibliographic record
Abstract
Phase III evidence in the shape of a series of randomized controlled trials and meta-analyses has shown that laparoscopic gastrectomy is safe and gives better short-term results with respect to the traditional open technique for early-stage gastric cancer. In fact, in the East laparoscopic gastrectomy has become routine for early-stage gastric cancer. In contrast, the treatment of advanced gastric cancer through a minimally invasive way is still a debated issue, mostly due to worries about its oncological efficacy and the difficulty of carrying out an extended lymphadenectomy and intestinal reconstruction after total gastrectomy laparoscopically. Over the last ten years the introduction of robotic surgery has implied overcoming some intrinsic drawbacks found to be present in the conventional laparoscopic procedure. Robot-assisted gastrectomy with D2 lymphadenectomy has been shown to be safe and feasible for the treatment of gastric cancer patients. But unfortunately, most available studies investigating the robotic gastrectomy for gastric cancer compared to laparoscopic and open technique are so far retrospective and there have not been phase III trials. In the present review we looked at scientific evidence available today regarding the new high-tech surgical robotic approach, and we attempted to bring to light the real advantages of robot-assisted gastrectomy compared to the traditional laparoscopic and open technique for the treatment of gastric cancer.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".