Muscular workload of veterinary students during simulated open and laparoscopic surgery: A pilot study*
Bibliographic record
Abstract
OBJECTIVE: To compare upper extremity muscle activity and workload between simulated open surgery, multiple port laparoscopic surgery (MLS), and single incision laparoscopic surgery (SLS) techniques in veterinary students. STUDY DESIGN: Pilot study. POPULATION: Veterinary students (n = 10) from years 1 to 4. METHODS: Bipolar skin surface electrodes were fixed bilaterally to the forearm flexor, forearm extensor, biceps brachii, triceps brachii, and upper trapezius muscles. Electromyography data were recorded during one repetition of 2 simulated surgical exercises via open surgery, MLS, and SLS. Participants completed a validated workload survey after each simulated surgical technique. Muscle activity and perceived workload were compared between surgical techniques with 1-way ANOVAs and Fisher's LSD post hoc tests. RESULTS: Muscle activity during peg transfer was higher with MLS and SLS compared to simulated open surgery in the right and left forearm extensors (both P < .0001), right (P < .0001) and left biceps (P = .0005), right triceps (P = .0004), and right upper trapezius muscles (P = .0211). Similar results were found for the right and left forearm extensors (both P < .0001), right (P = .0381) and left (P = .0147) forearm flexors, right biceps (P < .0001), and right triceps (P = .0004) during a simulated suture task. Participants found laparoscopic techniques more mentally demanding, physically demanding, complex, and stressful compared to a simulated open surgical technique. CONCLUSION: In veterinary students, average muscle activity and perceived workload were highest using MLS and SLS compared to an open surgical technique when performing simulated surgical exercises in a laparoscopic box trainer.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".