Pilot Evaluation of a Novel, Low-Cost, Simulation Model for Training and Assessment of Laparoscopic Intracorporeal Continuous Suturing
Bibliographic record
Abstract
Background: Laparoscopic intracorporeal continuous suturing is being employed in a growing number of minimally invasive procedures. However, there is a lack of adequate bench models for gaining proficiency in this complex task. The purpose of this study was to assess a novel simulation model for running suture. Methods: Participants were grouped as novice (LSN) or expert (LSE) at laparoscopic suturing based on prior experience and training level. A novel low-cost bench model was developed to simulate laparoscopic intracorporeal continuous closure of a defect. The primary outcome measured was time taken to complete the task. Videos were scored by independent raters for Global Operative Assessment of Laparoscopic Skills (GOALS). Results: Sixteen subjects (7 LSE and 9 LSN) participated in this study. LSE completed the task significantly faster than LSN (430 ± 107 vs 637 ± 164 seconds, P ≤ .05). LSN scored higher on accuracy penalties than LSE (Median 30 vs 0, P ≤ .05). Mean GOALS score was significantly different between the 2 groups (LSE 20.64 ± 2.64 vs LSN 14.28 ± 1.94, P < .001) with good inter-rater reliability (ICC ≥ .823). An aggregate score using the formula: Performance Score = 1200-time(sec)-(accuracy penalties x 10) was significantly different between groups with a mean score of 741 ± 141 for LSE vs 285 ± 167 for LSN ( P < .001). Conclusion A novel bench model for laparoscopic continuous suturing was able to significantly discriminate between laparoscopic experts and novices. This low-cost model may be useful for both training and assessment of laparoscopic continuous suturing proficiency.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".