191 Minimally Invasive General Surgery Landmark Trials and Lessons Learned - A Narrative Review
Bibliographic record
Abstract
Abstract Aims The aim of this review is to explore the history of minimally invasive (MI) surgery and landmark trials that guide modern practice. Methods A narrative review was conducted according to the four steps outlined by Demiris et al. Results MI surgery has become the gold-standard for many operations. Laparoscopic surgery has equivalent operative outcomes to open surgery with reduced recovery time and hospital stay. Randomised controlled trials (RCTs) investigating complex laparoscopic upper gastrointestinal oncological resections demonstrate reduced blood loss and postoperative pain. Within colorectal surgery, the CLASSIC and COREAN trials described comparable mortality and oncological clearance in MI colorectal cancer resections versus open surgery. Conversely, ALaCaRT and ACOSOG raised concerns about oncological results. Robotic surgery provides a high degree of instrument freedom and stabilises hand tremors, however substantial associated costs remain a barrier. Robotic cholecystectomy (RC) is a safe and effective tool, and RC outcomes are comparable to laparoscopic surgery in the elective setting; although more evidence is required for more complex disease outcomes. For rectal cancer, the ROLARR trial found no difference in complications, conversion rate, or circumferential margin positivity for robotic-assisted versus laparoscopic surgery. Recently, the Smart Tissue Autonomous Robot performed the first autonomous intestinal anastomosis on porcupines, outperforming expert surgeons for both consistency and accuracy, demonstrating the potential future of robotic surgery. Conclusion This review gives an overview of MI general surgery; demonstrating the advantages as well as some concerns for the different surgical techniques, and raises suggestions for future uses.
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 | 0.008 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".