Factors Associated with the Innate Orthopedic Ability of Veterinary Students
Bibliographic record
Abstract
Relatively little is known about the innate surgical ability of veterinary undergraduates. The objective of this study was to investigate if there were differences in the innate surgical ability of a cohort of 142 third-year veterinary undergraduate students to perform a series of simulated orthopedic surgical tasks, and whether specific factors influenced their innate ability. Participants performed four simulated surgical tasks; "depth of plunge"-an assessment of the "plunge" depth through foam when drilling through the trans cortex of a PVC pipe; "3-dimensional drilling"-an assessment of accuracy when drilling through a block of wood; "depth measurement"-an assessment of the ability to correctly measure the depth of holes in PVC pipe; and "fracture reduction"-where the speed and systematic reduction of a simulated fracture was assessed using a rubric score. Performance for each task was compared based on the responses to a survey. Results showed considerable variation in innate ability. Previous experience performing manual tasks and using a drill was associated with an improvement in students' ability to perform one of the four tasks (fracture reduction). Age, gender, handedness, videogame experience, building game experience, exposure to orthopedic surgery, or desire to pursue surgery as a career were not associated with student performance in any task. A learning curve was observed for the depth of plunge task. An increased target angle led to decreased drilling accuracy for the 3D drilling task. The innate ability of veterinary students to undertake simulated surgical tasks was largely unaffected by the previous experiences evaluated.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".