Innovation in gastrointestinal surgery: the evolution of minimally invasive surgery—a narrative review
Bibliographic record
Abstract
Background: Minimally invasive (MI) surgery has revolutionised surgery, becoming the standard of care in many countries around the globe. Observed benefits over traditional open surgery include reduced pain, shorter hospital stay, and decreased recovery time. Gastrointestinal surgery in particular was an early adaptor to both laparoscopic and robotic surgery. Within this review, we provide a comprehensive overview of the evolution of minimally invasive gastrointestinal surgery and a critical outlook on the evidence surrounding its effectiveness and safety. Methods: A literature review was conducted to identify relevant articles for the topic of this review. The literature search was performed using Medical Subject Heading terms on PubMed. The methodology for evidence synthesis was in line with the four steps for narrative reviews outlined in current literature. The key words used were minimally invasive, robotic, laparoscopic colorectal, colon, rectal surgery. Conclusion: The introduction of minimally surgery has revolutionised patient care. Despite the evidence supporting this technique in gastrointestinal surgery, several controversies remain. Here we discuss some of them; the lack of high level evidence regarding the oncological outcomes of TaTME and lack of supporting evidence for robotic colorectalrectal surgery and upper GI surgery. These controversies open pathways for future research opportunities with RCTs focusing on comparing robotic to laparoscopic with different primary outcomes including ergonomics and surgeon comfort.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".