Commercial Video Laparoscopic Trainers versus Less Expensive, Simple Laparoscopic Trainers: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
PURPOSE: Advancements in laparoscopic surgical simulation have led to technologically sophisticated but generally more costly surgical trainers. Given that higher costs can limit training institutions, an exploration of cost-effective alternatives is a worthwhile endeavor. We compared commercial video laparoscopic trainers and less expensive simple laparoscopic trainers to evaluate how they differ in facilitating the acquisition of laparoscopic skills in surgical trainees, as measured by laparoscopic task completion time. MATERIALS AND METHODS: We performed a comprehensive, systematic search of the literature, which yielded 1,091 citations after excluding duplicates. Ten articles were fully reviewed and 5 were included in the final analysis. Articles were reviewed to ensure that a comparison of video and simple laparoscopic trainers was present and laparoscopic tasks were examined. Quality assessment of studies was completed using a comprehensive checklist. We examined continuous data with calculation of the standardized mean difference. Performance times were pooled using a random effects model and the chi-square test for heterogeneity. Meta-analysis was done to compare post-training performance times between video and simple laparoscopic trainers for the 2 laparoscopic tasks of suturing and object transfer. RESULTS: We found no statistically significant difference in task completion time for video and simple laparoscopic trainers. Meta-analysis of the 7 laparoscopic tasks assessed by others favored video over simple laparoscopic trainers but this was not statistically significant (standardized mean difference -1.82, 95% CI -0.61-0.02, p = 0.07). CONCLUSIONS: Video and simple laparoscopic trainers are equally proficient for facilitating the acquisition of laparoscopic skills, suggesting that simple laparoscopic trainers may be a cost-effective alternative.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.012 | 0.003 |
| 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 | 0.001 |
| 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 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".