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 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".