Characterizing the Preferred Reporting Methods in Neuroradiology: A Multispecialty Survey
Bibliographic record
Abstract
Background: Report structures in radiology can be free-text or structured formats. There are currently no guidelines regarding optimal reporting structure for neuroradiological studies. Clear and efficient reports are essential to facilitating communication between healthcare providers. This project characterizes and compares preferred radiological reporting structures in neuroradiology among physicians. Methods: A REDCap survey including questions on practice environments, satisfaction with current reports, and preferences in report structures for 7 studies: MRI Lumbar Spine, MRI Sella, MRI Dementia, MRI Glioma, MRI Brain Metastases, CTA Head and Neck, and CT Unenhanced Brain was drafted and reviewed by radiologists. This anonymous survey collected responses from radiologists and physicians who read neuroradiology reports across the Greater Toronto Area. Results: The survey received 89 responses. Structured reports were preferred over free-text reports across all specialties for each study. Notably, a large proportion (37/44, 84.1%) preferred having structured reports for CTA head and neck. Preferences for MRI Brain Glioma were relatively mixed, with some respondents favouring free-text reports (8/24, 33.3%) and others preferring structured reports (13/24, 54.2%). Respondents preferring structured reports cited “ease in finding information” as the most common reason, while those favouring free-text reports chose “fewer unnecessary sections” most often. Conclusion: This study identifies opportunities to improve the organization and standardization of information in radiology reports. The consistent preference for structured reports highlights the need for guidelines to optimize radiological reporting and enhance communication between specialties.
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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 | 0.019 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".