Investigating the impact of innate dexterity skills and visuospatial aptitude on the performance of baseline laparoscopic skills in veterinary students
Bibliographic record
Abstract
OBJECTIVE: To determine if manual dexterity and visuospatial skills can be used to predict baseline laparoscopic surgery skills in veterinary students. STUDY DESIGN: Pilot study. METHODS: Veterinary students (n = 45) from years 1-4 volunteered for this study. An hour-long electronic questionnaire was completed by participants. The first section was used to collect demographics and information about prior nonsurgical experiences. The second section included 3 tests of visuospatial skills, including the Purdue Visualization of Rotations Test, Mental Rotations Test, and Raven's Advanced Progressive Matrices Test. Multiple tests were administered to assess innate dexterity, including the grooved pegboard test, indirect and direct zigzag tracking tests, and the 3Dconnexion proficiency test. Each dexterity test was performed once with the left hand and once with the right hand. The order of task performance was randomized. Basic laparoscopic skills were assessed using the validated fundamentals of laparoscopic surgery (FLS) peg transfer task. RESULTS: There was an association between left-handed grooved pegboard scores (95% CI -10046.36 to -1636.53, P-value = .008) and left-handed indirect zigzag tracking task (95% CI -35.78 to -8.20, P-value = .003) with FLS peg transfer scores. Individuals who reported playing videogames achieved higher scores on the FLS peg transfer task than those without videogame experience (95% CI 583.59 to 3509.97, P-value = .007). CONCLUSION: The results of this study suggest that dexterity was a better predictor of baseline laparoscopic skills than visuospatial skills in veterinary students.
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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 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".