Learning and application of intracorporal slipping knot techniques in minimally invasive surgery
Bibliographic record
Abstract
Abstract Aim Intracorporal knot tying (ICKT) and suturing in minimally invasive surgery (MIS) are key skills for advanced procedures. The best choice for an intracorporal slipping knot tying technique had not been defined. The aim of this study was to compare two intracorporal slipping knot techniques: the classical C‐loop technique (Variant I) and the square‐to‐slip technique without changing leadership hand (Variant II). Methods A laparoscopic box trainer including laparoscope was used for ICKT. A total of 120 slipping knots were evaluated in two groups with different levels of surgical education. The first group (n = 5) consisted of senior surgical physicians proficient in laparoscopic surgery. The second group (n = 10) comprised medical students without any prior experience in laparoscopic surgery. The medical student group received a 1‐hour hands‐on training session. Participants were assessed using the Global Rating Scale of the Objective Structured Assessment of Technical Skills, procedural implementation, knot quality, and task time. Results In the medical student group, performance in all parameters was greater for Variant I compared with Variant II (P < .005). Contrary to the students group, senior physicians demonstrated significantly faster task time in Variant II compared with Variant I (P = .001). No significant differences were observed in the remaining parameters. Conclusion For novices, the intracorporal slipping knot is easier to learn with the C‐loop technique (Variant I) than with the square‐to‐slip technique (Variant II). For surgeons experienced in MIS, the square‐to‐slip technique without changing leadership hand (Variant II) was superior to the slipping knot in the C‐loop technique (Variant I) only by time savings. The knot quality, as the most important variable, showed equally good results between both ICKT techniques.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".