Style and Content of CT and MR Imaging Lumbar Spine Reports: Radiologist and Clinician Preferences
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Several studies have examined clinician preferences regarding the style of body sonography and CT reports. Our study is the first to examine clinicians' and radiologists' preferences in lumbar spine CT and MR imaging reports with respect to content and format and specific components such as management suggestions by the radiologist. MATERIALS AND METHODS: A spine report survey, which consisted of 3 case scenarios, each with 6 different reports varying in content and format, was mailed to clinicians and radiologists. Their preferences regarding content, format, and management suggestions were gathered. RESULTS: A total of 89 clinicians (49%) and 31 radiologists (53%) responded. Both clinicians and radiologists preferred reports with moderate or detailed instead of limited content (P < .01). Itemized and prose formats were equally acceptable to clinicians and radiologists. Although both groups identified moderate CT technique description as ideal, more clinicians valued the inclusion of the quality of a CT study (P < .001). Specialists preferred reports with greater detail but no recommendations, whereas family physicians preferred less detail but wanted specific management suggestions (P < .01). Neuroradiologists (75%-100%) were more likely to provide management suggestions than non-neuroradiologists (23%-59%). CONCLUSIONS: Clinicians favored lumbar spine CT and MR imaging reports with detailed content in either itemized or structured prose formats, irrespective of the modality or the extent of abnormalities reported. Family physicians preferred management suggestions from the radiologists. Specialists, however, preferred a review of the radiologic findings and an opinion without specific recommendations. To optimize patient care, radiologists should be mindful of these preferences and consider tailoring their reports to their audiences.
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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 | 0.015 | 0.098 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".