Visuospatial skills are better predictors than dexterity for basic ultrasonographic and fluoroscopic skills in veterinary students
Bibliographic record
Abstract
Radiological skills including ultrasonography and fluoroscopy, require a combination of manual dexterity and visuospatial skill to develop competency. The ability to detect veterinary students with an interest in radiology but who are deficient in these skills, may permit more individual adaptations to training programs, allowing for students training in radiology to achieve maximal potential. The objective of this cohort study was to investigate whether innate dexterity and visuospatial skill could be used to predict performance of basic ultrasound and fluoroscopic skills in veterinary students. Fifty veterinary students from the Ontario Veterinary College completed three tests of visuospatial ability, two tests of manual dexterity, a three-dimensional mouse task, an ultrasound skill-testing task, and a fluoroscopic skill-testing task. Students who reported chopstick use completed the non-dominant hand ultrasound task significantly faster than students who did not (P = 0.001). There was a significant positive association between scores on the Mental Rotations Test and time to complete the non-dominant hand ultrasound task (P = 0.011) and fluoroscopy task (P = 0.029). No variables were associated with time to complete the dominant hand ultrasound task. The results of this study suggest that visuospatial skill, as assessed by the Mental Rotations Test, is a better predictor of baseline ultrasound and endovascular fluoroscopy skill than dexterity, with the exception of reported chopstick use, in veterinary students. Visuospatial skills can be developed and may be useful to include in the veterinary curriculum for students that are deficient, or students entering a field such as diagnostic imaging.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.004 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".