Educational Interventions to Improve Cytology Visual Diagnostic Reasoning Measured by Eye Tracking
Bibliographic record
Abstract
The teaching of visual diagnostic reasoning skills, to date, has been conducted in a largely unstructured apprenticeship manner. The purpose of this study was to assess if the introduction of two educational interventions improved the visual diagnostic reasoning skills of novices. These were (1) the active use of key diagnostic features and (2) image repetition. A pre-test and post-test research design was used to compare the two teaching interventions to a traditional teaching group and an expert group using eye tracking as an assessment method. The time to diagnosis and the percentage of time spent viewing an area of diagnostic interest (AOI) were compared using independent t-tests, paired t-tests, and analysis of covariance (ANCOVA). Diagnostic accuracy as a dichotomous variable was compared using Chi-square tables. Students taught in an active-learning manner with image repetition behaved most like experts, with no significant difference from experts for percentage of time spent in the AOIs and a significantly faster time to diagnosis than experts (p<.017). Our results from the educational interventions suggest a greater level of improvement in the eye tracking of students that were taught key diagnostic features in an active-learning forum and were shown multiple case examples.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".