Quantifying Novice and Expert Differences in Visual Diagnostic Reasoning in Veterinary Pathology Using Eye-Tracking Technology
Bibliographic record
Abstract
Visual diagnostic reasoning is the cognitive process by which pathologists reach a diagnosis based on visual stimuli (cytologic, histopathologic, or gross imagery). Currently, there is little to no literature examining visual reasoning in veterinary pathology. The objective of the study was to use eye tracking to establish baseline quantitative and qualitative differences between the visual reasoning processes of novice and expert veterinary pathologists viewing cytology specimens. Novice and expert participants were each shown 10 cytology images and asked to formulate a diagnosis while wearing eye-tracking equipment (10 slides) and while concurrently verbalizing their thought processes using the think-aloud protocol (5 slides). Compared to novices, experts demonstrated significantly higher diagnostic accuracy (p <.017), shorter time to diagnosis (p <.017), and a higher percentage of time spent viewing areas of diagnostic interest (p <.017). Experts elicited more key diagnostic features in the think-aloud protocol and had more efficient patterns of eye movement. These findings suggest that experts' fast time to diagnosis, efficient eye-movement patterns, and preference for viewing areas of interest supports system 1 (pattern-recognition) reasoning and script-inductive knowledge structures with system 2 (analytic) reasoning to verify their diagnosis.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".