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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".