Tailoring Decision Support to Suit User Needs: A Diagnostic Imaging Example
Bibliographic record
Abstract
Unnecessary diagnostic imaging (DI) examinations raise concerns for patient safety and place stress on human and financial resources. To reduce unnecessary DI examinations, several Canadian pilot studies have investigated how decision support systems (DSS) could be utilized. Based on interview results from our previous research, in addition to a literature review, themes emerged that influenced the features and design of a DI DSS prototype. Features include having the referring professional indicate how the results of the examination will be utilized (i.e. for diagnosis or patient management), increasing communication between referring physicians/nurse practitioners and radiologists, and displaying previous DI examinations (or orders that are scheduled to take place) to avoid duplicate orders. Presenting a patient's cumulative radiation exposure, and having resources for information support to guide physicians through challenging clinical decisions are two other features included in the DSS prototype. By incorporating physician perspectives and current literature into the design, this DSS aims to promote the appropriate use of DI resources by supporting physicians and nurse practitioners in their DI ordering practices.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".