Providing comparison normal examples alongside pathologic thoracic radiographic cases can improve veterinary students’ ability to identify abnormal findings or diagnose disease
Bibliographic record
Abstract
Learning by comparison is a frequently employed education strategy used across many disciplines and levels. Interpreting radiographs requires both skills of perception and pattern recognition, which makes comparison techniques particularly useful in this field. In this randomized, prospective, parallel-group study, students enrolled in second and third-year radiology veterinary courses were given a case-based thoracic radiographic interpretation assignment. A cohort of the participants was given cases with side-by-side comparison normal images while the other cohort only had access to the cases. Twelve cases in total were presented to the students, with 10 cases depicting examples of common thoracic pathologies, while 2 cases were examples of normal. Radiographs of both feline and canine species were represented. Correctness of response to multiple choice questions was tracked, as was year and group (group 1: non compare, Control; group 2: compare, Intervention). Students assigned to group 1 had a lower percentage of correct answers than students assigned to group 2 (45% Control vs. 52% Intervention; P = 0.01). This indicates that side-by-side comparison to a normal example is helpful in identifying disease. No statistical significance was noted for the correctness of responses according to the year of training (P = 0.90). The overall poor performance on the assignment, regardless of group or year, shows that students in the early years of undergraduate veterinary radiology training struggle with the interpretation of common pathologies, likely a result of a lack of exposure to a multitude of cases and normal variants.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".