Crafting diversity in radiology image stack scrolling
Bibliographic record
Abstract
To make a single diagnosis, today's radiologists must examine thousands of images; yet little effort has been put into refining this time-consuming, repetitive task. Meanwhile, automatic or radiologist-generated annotations may impact how radiologists navigate image stacks as they review lesions of interest. Observation and/or interviews of 19 radiologists revealed that stack scrolling dominated the resulting task examples. We iteratively crafted and obtained radiologist feedback for a variety of prototypes, then evaluated their scrolling and annotation-review support for lay users. With a simplified stack seeded with correct / incorrect annotations, we compared the effect of four scrolling techniques (traditional scrollwheel and click-and-drag, plus sliding-touch, and tilt rate control) and visual vs. haptic annotation cues on scrolling dynamics, detection accuracy and subjective factors. Scrollwheel was fastest overall, and combined visual / haptic annotation cues sped target-finding relative to either modality alone. We share insights on integrating our findings into radiologist practice.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".