The Use of Simulators for Teaching Fine Needle Aspiration Cytology in Veterinary Medicine
Bibliographic record
Abstract
Fine needle aspiration (FNA) is widely used by veterinary practitioners, being taught mostly by observation. Simulators are known to enhance students' learning of practice skills, but to our knowledge, FNA simulators have never been assessed in veterinary medicine. Fifty-one undergraduate students with no prior experience in cytology were randomly assigned to two groups that practiced on either a box simulator (with artificial nodules) or a fruit (banana). An in-class flip was followed, in which students first observed a FNA video tutorial and then used their assigned simulator for 15 minutes maximum. Students then attempted a FNA on an animal model and were evaluated through an objective structured clinical examination (OSCE). Learning outcomes of each model was compared through questionnaires, OSCE pass rates, and quality of produced smears. After observing the video, no student reported being able to conduct a FNA on a live animal, whereas most assured that they would be able to do so after using a simulator. Students practiced more on the box model (14.8 ± 0.8 min) than on the fruit (8.5 ± 2.2 min). At evaluation, students who had practiced on the box had more puncturing accuracy than those who had practiced on the fruit. Still, no differences in OSCE pass rates existed. Simulation models thus were effective for learning FNA, but the box simulator seemed to be more successful than the fruit in terms of deliberate practice. This appears to have a positive effect on students' puncturing accuracy, which has clinical relevance.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".