<title>Workflow oriented hanging protocols for radiology workstation</title>
Bibliographic record
Abstract
The goal is to provide a smooth, efficient and automatic display for interpretation of medical images by using a new generation of hanging protocols (HPs). HPs refer to a set of rules defining the way images are arranged on the computer screen immediately after opening a case. HPs usually include information regarding placement of the sequences, viewing mode, layout, window width and level (W/L) settings, zoom and pan. We present the results of a survey of 8 radiologists on (1) the necessity of using HPs, (2) the applicability of a hierarchical organization of HPs and (3) the number of HPs required for interpretation. We discuss some limitations and challenges associated with the HP including automatic placement of the series on the screen despite non-standard series labeling, generation of pseudo-series, creation of the 'study context' and identification of relevant priors, and image display standardization with automatic orientation and shuttering. The paper also addresses the HP selection based on the workstation's hardware such as number and type of monitors, size of the study, and presence of image processing routines tailored to the information needs and level of expertise of particular users. Our 'heads-up' approach is meant to free the user's conscious processing for reasoning such as detection of patterns so allowing for the execution of the tasks in an efficient, yet highly adaptive manner, sensitive to shifting concepts. Automation of routine tasks is maximized through the creation of shortcuts and macros embedded in features like multi-stage HP.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.221 | 0.142 |
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".