Usefulness of a Computer-Aided Learning Module for Teaching Radiology of the Equine Foot to Clinical Veterinary Students
Bibliographic record
Abstract
Lameness in horses resulting from foot pathology is very common. When investigating the cause of a lameness localised to the foot, the first step is most frequently radiographic imaging. Therefore, being able to identify normal anatomy and recognise pathology on radiographs is important for a veterinary medicine student to learn. Computer-aided learning (CAL) is becoming increasingly utilised in the teaching of students on medicine-related courses, especially post-COVID where online learning has been continued in hybridisation with in-person teaching.In this study, a low-cost CAL module was created focusing on anatomy and pathology of the equine foot on radiographic images and testing was carried out to evaluate how beneficial students found this resource for their learning. There were two research questions: 1. Can a useful CAL module be produced at low cost? 2. Will this CAL module function to increase student confidence? The CAL module was produced at no cost; similar CAL modules could be easily re-created using a similar module at a low-to-no cost. Three skills were reviewed: recognition of normal anatomy, identification of pathology, and selection of appropriate radiographic views for investigation of specific pathologies. A statistically significant increase in confidence of students' ability to recognise pathology and to select radiographic views for investigating specific pathologies when comparing pre- and post-resource confidence. Anecdotally there was a positive response to the resource: users found it useful for the intended purpose. Therefore, a useful CAL module was produced at low cost, and did indeed increase students' confidence in some areas investigated.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".