Evaluation of a Novel Modified Suture Material Designed to Facilitate Intracorporeal Knot Tying during Laparoscopic Surgery
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Laparoscopic intracorporeal knot tying in minimally invasive surgery is an advanced skill. Mastering this skill is an arduous process with a long learning curve. While recent advances in instrumentation have allowed easier suturing and tying, until now, no attempts have been made to modify the suture material in order to facilitate this process. We present an evaluation of a novel modified suture material designed to allow inexperienced surgical residents to tie intracorporeal knots laparoscopically using conventional laparoscopic needle drivers. SUBJECTS AND METHODS: Surgical residents with no prior experience in laparoscopic surgery were invited to take part in this investigation. Each of the 14 participants was given a 10-minute demonstration of laparoscopic intracorporeal knot tying and then allowed a mentored practice session of 10 minutes. In the first trial, they were then randomized to tie a laparoscopic knot with either a standard or a modified dry suture. Time and accuracy scores were recorded. They then performed the same task with the other type of suture. On the second trial, wet standard and modified sutures were used, and the order of the sutures used in the first trial was reversed. RESULTS: The average time taken to tie an intracorporeal knot laparoscopically was significantly less when the modified suture was used in both dry and wet conditions (162.71 +/- 10.79 seconds v 270.86 +/- 22.76 seconds; P = 0.0039, and 123.29 +/- 4.70 seconds v 247.57 +/- 23.17 seconds; P = 0.0032, respectively). No significant difference in accuracy scores was noted with the two sutures. CONCLUSIONS: Our modified suture design allowed inexperienced surgical residents to perform intracorporeal laparoscopic knot tying on average faster than the standard suture did. The concept of modifying suture design to facilitate laparoscopic suturing and knot tying deserves further investigation and development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".