Evaluation of a Training Model to Teach Veterinary Students a Technique for Injecting the Jugular Vein in Horses
Bibliographic record
Abstract
In this study, a newly-developed model for training veterinary students to inject the jugular vein in horses was evaluated as an additional tool to supplement the current method of teaching. The model was first validated by 19 experienced equine veterinarians, who judged the model to be a realistic and valuable tool for learning the technique. Subsequently, it was assessed using 24 students who were divided randomly into two groups. The injection technique was taught conventionally in a classroom lecture and a live demonstration to both groups, but only group 1 received additional training on the new model. All participants filled out self-assessment questionnaires before and after group 1 received training on the model. Finally, the proficiency of both groups was assessed using an objective structured clinical evaluation (OSCE) on live horses. Students from group 1 showed significantly improved confidence after their additional training on the model and also showed greater confidence when compared to group 2 students. In the OSCE, group 1 had a significantly better score compared to group 2: the median (with inter-quartile range) was 15 (0.7) vs. 11.5 (2.8) points out of 15, respectively. The training model proved to be a useful tool to teach veterinary students how to perform jugular vein injections in horses in a controlled environment, without time limitations or animal welfare concerns. The newly developed training model offers an inexpensive, efficient, animal-sparing way to teach this clinical skill to 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".