Bibliographic record
Abstract
Since the 1980s, radiologists have started to interpret digital radiographs using modern computer systems, a process known as softcopy reading. For softcopy reading, Hanging Protocols are used to automatically arrange images for interpretation upon opening a case, thus minimizing the need for physicians to manipulate images. We have developed a strategy, called HP++, which extends current hanging protocols with support for 'scenario-based' interpretation, matching the radiologist's workflow and ensuring a chronological presentation of information. We hypothesized that HP++ significantly reduces off-image eye fixations, the interpretation time, and the frequency and complexity of user input. We validated our hypothesis with inexpensive usability studies based on an abstraction of the radiologist's task, transferred to novice subjects. For a radiology look-alike task, we compared the performance of 20 graduate students using our HP++ based interaction technique with their performance using a conventional interaction technique. We observed a 15% reduction in the average interpretation time using the staged approach, with one third fewer interpretation errors, two thirds fewer mouse clicks, and over 65% less eye gaze over the workstation controls. User satisfaction with the staged interface was significantly higher than with the traditional interface. Preliminary external validation of these results with physician subjects indicate our usability results transfer to radiology softcopy reading. We conclude that designing radiology workstations with support for HP++ can improve the performance of workstation users.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.129 | 0.069 |
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".