Errors in Radiographic Interpretation Made by Veterinary Students
Bibliographic record
Abstract
As a means of identifying student weaknesses in radiographic interpretation that could be used as foci for teaching, a cohort of 96 students joining the final-year radiology rotation were randomly allocated to one of three radiographic interpretation quizzes, each based on radiographs of small-animal patients together with the signalment and a brief, relevant history. Students' quiz scores were analyzed by multiple logistic regression, using an outcome variable with the score for each item as numerator and maximum possible mark as denominator. Students' median quiz score was 49% of the maximum (range 23-80%). Students were more likely to gain a mark for items based on abnormal radiographs than for those based on normal radiographs (odds ratio 3.4, p < 0.001). Skeletal radiographs were associated with lower scores (OR 0.75, p = 0.03). The fewest marks were awarded for interpretation of a radiograph of a normal canine stifle and interpretation of a radiograph of a normal canine pelvis; these items were misinterpreted as abnormal by 86% and 80% of the students, respectively. Students' tendency to over-interpret normal radiographs may reflect a lack of knowledge of radiographic anatomy or an unrealistically high expectation that the radiographs are abnormal.
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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 | 0.008 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.001 |
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".