Development and Evaluation of Two Canine Low-Fidelity Simulation Models
Bibliographic record
Abstract
Two self-made low-fidelity models for simulation of canine intubation and canine female urinary catheterization were developed and evaluated. We used a study design that compares acquired skills of two intervention groups and one control group in a practical examination. Fifty-eight second-year veterinary medicine students received a theoretical introduction to intubation and were randomly divided into three groups. Group I (high-fidelity) was then trained on a commercially available Intubation Training Manikin (item #2006, Veteffects), group II (low-fidelity) was trained on our low-fidelity model, and group III (text) read a text describing intubation of the dog. Forty-seven fifth-year veterinary medicine students followed the same procedure for training urinary catheterization using the commercially available Female Urinary Catheter Training Manikin (Paws 2 Claws), our self-made model, and text. Outcomes were assessed in a practical examination on a cadaver using an Objective Structured Clinical Examination (OSCE) checklist. Considering a value of p≤.05 significant, intervention groups performed significantly better than the text groups. Group I (high-fidelity) and group II (low-fidelity) showed no significant differences (p≤.684, intubation; p≤.901, urinary catheterization). We thereby conclude that low-fidelity models can be as effective as high-fidelity models for clinical skills training.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".