Comparison between Training Models to Teach Veterinary Medical Students Basic Laparoscopic Surgery Skills
Bibliographic record
Abstract
The objective of this study was to compare the effectiveness of two different laparoscopic training models in preparing veterinary students to perform basic laparoscopic skills. Sixteen first- and second-year veterinary students were randomly assigned to a box trainer (Group B) or tablet trainer (Group T). Training and assessment for both groups included two tasks, "peg transfer" and "pattern cutting," derived from the well-validated McGill University Inanimate System for Training and Evaluation of Laparoscopic Skills. Confidence levels were compared by evaluating pre- and post-training questionnaires. Performance of laparoscopic tasks was scored pre- and post-training using a rubric for precision and speed. Results revealed a significant improvement in student confidence for basic laparoscopic skills (p<.05) and significantly higher scores for both groups in both laparoscopic tasks (p<.05). No significant differences were found between the groups regarding their assessment of the video quality, lighting, and simplicity of setup (p=.34, p=.15, and p=.43, respectively). In conclusion, the low-cost tablet trainer and the more expensive box trainer were similarly effective in preparing pre-clinical veterinary students to perform basic laparoscopic skills on a model.
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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".