Attitudes and experience of urology trainees in interpreting prostate magnetic resonance imaging
Bibliographic record
Abstract
INTRODUCTION: Multiparametric magnetic resonance imaging (mpMRI) has resulted in accurate prostate cancer localization and image-guided targeted sampling for biopsy. Despite its more recent uptake, knowledge gaps in interpretation and reporting exist. Our objective was to determine the need for an educational intervention among urology residents working with mpMRIs. METHODS: We administered an anonymous, cross-sectional, self-report questionnaire to a convenience sample of urology residents in U.S. and Canadian training programs. The survey included both open- and closed-ended questions employing a five-point Likert scale. It was designed to assess familiarity, exposure, experience, and comfort with interpretation of mpMRI. RESULTS: Fifty-three surveys were completed by residents in postgraduate years (PGY) 1-5 and of these, only 12 (23%) reported any formal training in mpMRI interpretation. Most residents' responses demonstrated significant experience with prostate biopsies, as well as familiarity with reviewing mpMRI for these patients. However, mean (± standard deviation [SD]) Likert responses suggested a relatively poor understanding of the components of Prostate Imaging-Reporting and Data System (PI-RADS) v2 scoring for T2-weighted films (2.45±1.01), diffusion-weighted imaging (DWI) films (2.26±0.90), and dynamic contrast-enhanced (DCE) films (2.21±0.99). Similar disagreement scores were observed for questions around interpretation of the different functional techniques of MRI images. Residents reported strong interest (4.21±0.91) in learning opportunities to enhance their ability to interpret mpMRI. CONCLUSIONS: While mpMRI of the prostate is a tool frequently used by care teams in teaching centers to identify suspicious prostate cancer lesions, there remain knowledge gaps in the ability of trainees to interpret images and understand PI-RADS v2 scoring. Online modules were suggested to balance the needs of trainee education with the residency workflow.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".