Creating patient-centered radiology reports to empower patients undergoing prostate magnetic resonance imaging
Bibliographic record
Abstract
INTRODUCTION: As we progress to an era when patient autonomy and shared decision-making are highly valued, there is a need to also have effective patient-centered communication tools. Radiology reports are designed for clinicians and can be very technical and difficult for patients to understand. It is important for patients to understand their magnetic resonance imaging (MRI) report in order to make an informed treatment decision with their physician. Therefore, we aimed to create a patient-centered prostate MRI report to give our patients a better understanding of their clinical condition. METHODS: A prototype patient-centered radiology report (PACERR) was created by identifying items to include based on opinions sought from a group of patients undergoing prostate MRI and medical experts. Data was collected in semi-structured interviews using a salient belief question. A prototype PACERR was created in collaboration with human factors engineering and design, medical imaging, biomedical informatics, and cancer patient education groups. RESULTS: Fifteen patients and eight experts from urology, radiation oncology, radiology, and nursing participated in this study. Patients were particularly interested to have a report with laymen terms, concise language, contextualization of values, definitions of medical terms, and next course of action. Everyone believed the report should include the risk of MRI findings actually being cancer in the subsequent biopsy. CONCLUSIONS: A prostate MRI PACERR has been developed to communicate the most important findings relevant to decision-making in prostate cancer using patient-oriented design principles. The ability of this tool to improve patient knowledge and communication will be explored.
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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 | 0.017 | 0.044 |
| 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 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".