Prostate Cancer Detection by Novice Micro-Ultrasound Users Enrolled in a Training Program
Bibliographic record
Abstract
Objective Micro-ultrasound is an imaging modality used to visualize and target prostate cancer during transrectal or transperineal biopsy. We evaluated the effectiveness of a micro-ultrasound training program and estimated the learning curve for prostate biopsy. Methods A training program registry was assessed for the rate of clinically significant prostate cancer (csPCa, grade group ≥ 2), negative predictive value, and specificity at each stage of the program. Nine metrics of biopsy quality were evaluated in 4 stages for each practitioner. Non-linear fitting and logistic regression models were used to evaluate the time-course of these metrics over training. Results Thirteen practitioners from 8 institutions completed stages 1 to 3 of the program, and 9 completed all 4 stages. Over 1190 micro-ultrasound biopsy procedures were performed. Detection of csPCa increased from 40% to 57% from stage 1 to stage 4 (P < 0.01). Stage 4 “expert” level was independently associated with higher detection of csPCa when correcting for overall risk factors (OR 1.95; P = 0.03). Limitations include the retrospective analysis and variation in biopsy protocols. Conclusion The micro-ultrasound training program was effective in improving biopsy quality and rate of csPCa detection. The presented learning curve provides an initial guide for acquiring expertise with real-time micro ultrasound image-guided biopsy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".