Evaluation of a Training Model to Teach Skills Associated with Esophagostomy Tube Placement in Companion Animals
Bibliographic record
Abstract
Models and simulations are used in veterinary education to allow students to practice surgical skills in order to obtain clinical competence. Further development of models is also driven by the requirement of veterinary institutions to reduce the use of animal patients in teaching (live or cadaver). Esophagostomy tube placement is a common therapeutic procedure performed in companion animal critical care cases, and a model was developed to help teach this skill. Validity evidence was collected and analyzed to evaluate this model at the University of Surrey. Veterinarians ( n = 14) provided content validity evidence on using the model, and students ( n = 19) provided further construct evidence. Students were taught the skill on either a model or a cadaver. These students were then assessed on a cadaver the following week. Global rating scales were used as a measure of performance, and data were recorded on confidence ratings after both teaching and assessment. Comparisons of the global rating scales and confidence levels were evaluated for both the model and cadaver-taught groups. There were no statistical differences in the performance data or confidence levels of the two groups. Most of the veterinarians believed the model was easy to use (13/14), had realistic landmarks (11/14), and was a suitable alternative to learning the skill than in the animal patient (12/14). The esophagostomy tube model is a low-cost, easy-to-make alternative to help teach aspects of this skill before performing on an animal patient.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".