Exploring the Benefits of Stroboscopic Technology and Guided Visualization in Teaching Suturing Techniques to Veterinary Medicine Students
Bibliographic record
Abstract
Suturing is widely regarded to be a core competency of veterinary education. With curricular requirements expanding, training interventions that improve students’ suturing skills without added time would be valuable. This study evaluated the effects of stroboscopic visual resistance training, a technique using intermittent occlusion of vision, and guided visualization on suturing technique and speed. Students’ anxiety levels were also assessed. Twenty-nine veterinary students with no prior surgical experience were divided into stroboscopic, visualization and control groups. Simple interrupted, simple continuous, and cruciate patterns were taught by an American College of Veterinary Surgeons (ACVS)-boarded veterinarian in week 1, and students also took an anxiety test at this time. One-hour-long tutored practice sessions were held for each group in weeks 2, 3, 4 and 6, and assessments were conducted in weeks 5 and 7. Assessments were conducted by a second ACVS-boarded veterinarian blinded to group assignments. Students in the stroboscopic training group had faster suturing times compared to students in the control group for the cruciate pattern at week 5 ( p = 0.001) and week 7 ( p = 0.006), and faster times compared to students in the visualization group at week 5 ( p = 0.002). Students in the stroboscopic training group had faster suturing times than the control group ( p = 0.005) for the simple continuous pattern at week 7. No significant differences were observed in anxiety. There was no significant difference in technique scores for any group with any pattern at any time point. Stroboscopic training may result in faster suturing times without deterioration of suturing technique.
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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 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".