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 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 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".