Influence of Teaching Satisfaction of Search Interpretation Errors on Detection of Radiographic Edge-and-Corner Lesions by Fourth-Year Veterinary Students
Bibliographic record
Abstract
Edge-and-corner (E&C) pathology is defined as clinically relevant findings in diagnostic imaging that are located at the physical periphery of studies and thus easily overlooked. Satisfaction of search is a perceptive interpretation error which can compound the difficulty of detecting E&C lesions. Guiding veterinary students to systematically identify these lesions would likely benefit their training, and the authors sought to determine whether teaching the concept of satisfaction of search could influence students' ability to detect E&C lesions. Sixty-five students beginning their clinical radiology rotation were recruited and allocated into treatment, placebo, and control groups. All were taught systematic imaging review techniques, though only the treatment group was taught about satisfaction of search error. A radiographic interpretation quiz was administered to assess students' ability to detect E&C lesions, determine whether awareness of satisfaction of search error impacts E&C lesion detection, and assess general preparation for the rotation based on application of knowledge from pre-clinical coursework. Additional associations between quiz performance and grade point average (GPA), pre-clinical radiology grade, veterinary school of matriculation, and weeks of clinical year experience were evaluated. No significant difference in detection of E&C lesions was found between any groups, though GPA, radiology course grade, and school of matriculation were significantly associated with general quiz performance. Results indicate that E&C lesion detection is a difficult task for students, that brief, lecture-based teaching of satisfaction of search error does not influence E&C lesion detection, and that pre-clinical grades at the authors' institution are predictive of imaging rotation preparedness.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".