Accuracy and Confidence in Performing Canine Stifle Goniometry was Similar between Simulation-Model or Traditional Textbook Trained Veterinary Students
Bibliographic record
Abstract
Goniometry is an essential skill used in veterinary rehabilitation settings to monitor orthopedic conditions. Our objectives were to create a normal canine stifle goniometry model and to compare students’ confidence and accuracy in performing goniometry with exposure to either the model or traditional teaching methods. We hypothesized that students would demonstrate goniometry skills more confidently and accurately after using a simulation model than those given traditional materials. A flexible model of a canine stifle was made. Twenty-eight veterinary students (8 clinical, 20 pre-clinical) prepared with either instructional material from a textbook ( n = 15) or access to the stifle model ( n = 13), and then assessed when performing goniometry (live dog). Students completed pre- and post-surveys where they indicated their confidence and anxieties. Statistical analyses included thematic analysis, descriptive statistics, and Chi-square analyses (significant at p ≤ .05). There was no difference in goniometry assessment or anatomy palpation scores between the model and reading groups. Clinical students ( n = 8) achieved higher scores in goniometry assessment ( p = .01) and anatomy palpation ( p = .04). Students were more confident when identifying their anatomical landmarks after using prep materials as compared to before using the prep materials ( p = .03), but only averaged identification of 3 out of 5 landmarks. Half could not correctly read the goniometer. In general, learning with models was preferred by all. There was no difference in learning between the model and textbook, so either can be used based on student preference. Further goniometer instructions should be provided. Anatomy of live dogs should be assessed more frequently pre-clinically.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".