Design and Validation of a Simulator for Feline Cephalic Vein Cannulation—A Pilot Study
Bibliographic record
Abstract
In recent years there has been an increased use of alternative methods for teaching veterinary clinical skills, since ethical considerations preclude the use of live animals for demonstration or practice of many procedures. Skills training on cats (i.e., feline venipuncture) is a particularly challenging area. This study aimed to develop a simulator for cephalic venipuncture in cats and to validate this simulator using questionnaires answered by undergraduate students and experienced veterinarians. The simulator was developed to provide an experience that was close to reality, including an artificial blood system that flows through the catheter when venipuncture is correctly performed, while at the same time using simple methodology and accessible materials so that it could be reproduced in other universities. The experienced vets agreed (44.4%) or strongly agreed (55.6%) that the simulator was good for venipuncture training, and the most useful feature was the experience of catheter manipulation and fixation on the cat's limb. All the students agreed that the practical class with the simulator was important for learning this skill. Both groups (students and experienced veterinarians) unanimously agreed that it is important to train using a simulator before trying the procedure on a live cat. This simulator offers undergraduate students an alternative way to learn and practice venipuncture in cats helping to reduce the use of live animals in practical classes.
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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 | 0.016 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".