Learning of Intracorporal Knot Tying in Minimally Invasive Surgery by Video or Expert Instruction
Bibliographic record
Abstract
INTRODUCTION: Minimally invasive surgery skill laboratories are indispensable in training, especially for complex procedural skills such as intracorporal suturing and knot tying (ICKT). However, maintaining a laboratory is expensive, and specially trained teachers are in short supply. During the COVID-19 pandemic, in-person instruction has reduced to almost zero, while model learning via video instruction (VID) has become an integral part of medical education. The aim of this study was to compare the learning effectiveness and efficiency of ICKT by laparoscopically inexperienced medical students through video versus direct expert instruction. MATERIALS AND METHODS: = 30). A laparoscopic box trainer including laparoscope was used for ICKT. Objective Structured Assessment of Technical Skills (OSATS), knot quality, and total ICKT time were the parameters for the assessment in this study. Proficiency criteria were also defined for these parameters. RESULTS: Students in the EXP group performed significantly better in OSATS-procedure-specific checklist (PSC) and knot quality compared with students in the VID group, with no difference in task time. Of the students who reached the proficiency criteria for OSATS-PSC and knot quality, those in the EXP group required fewer attempts to do so than those in the VID group. Students in both groups improved significantly in all parameters over the first hour of evaluation. CONCLUSION: For the laparoscopically inexperienced, training in ICKT through expert instruction presents an advantage compared with video-based self-study in the form of faster understanding of the procedure and the associated consistent implementation of good knot quality. Both teaching methods significantly improved participants' ICKT skills.
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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 | 0.000 | 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) | 0.003 | 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".