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 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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".