Identification of Distinct Visual Scan Paths for Pathologists in Rare-Element Search Tasks
Bibliographic record
Abstract
Background The search for rare elements, like mitotic figures, is crucial in pathology. Combining digital pathology with eye-tracking technology allows for the detailed study of how pathologists complete these important tasks. Objectives To determine if pathologists have distinct search characteristics in domain- and nondomain-specific tasks. Design Six pathologists and six graduate students were recruited as observers. Each observer was given five digital “Where's Waldo?” puzzles and asked to search for the Waldo character as a nondomain-specific task. Each pathologist was then given five images of a breast digital pathology slide to search for a single mitotic figure as a domain-specific task. The observers’ eye gaze data were collected. Results Pathologists’ median fixation duration was 244 ms, compared to 300 ms for nonpathologists searching for Waldo ( P < .001), and compared to 233 ms for pathologists searching for mitotic figures ( P = .003). Pathologists’ median fixation and saccade rates were 3.17/second and 2.77/second, respectively, compared to 2.61/second and 2.47/second for nonpathologists searching for Waldo ( P < .001), and compared to 3.34/second and 3.09/second for pathologists searching for mitotic figures ( P = .222 and P = .187, respectively). There was no significant difference between the two cohorts in their accuracy in identifying the target of their search. Conclusions When searching for rare elements during a nondomain-specific search task, pathologists’ search characteristics were fundamentally different compared to nonpathologists, indicating pathologists can rapidly classify the objects of their fixations without compromising accuracy. Further, pathologists’ search characteristics were fundamentally different between a domain-specific and nondomain-specific rare-element search task.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".