Detection of Clinically Significant Prostate Cancer Using Micro-Ultrasound vs Magnetic Resonance Imaging/Ultrasound Fusion Biopsy: A Propensity-Weighted Comparative Study
Bibliographic record
Abstract
PURPOSE: We compare the detection rates of clinically significant prostate cancer (csPCa) between cognitively targeted micro-ultrasound-guided biopsy (MB) and MRI/ultrasound fusion-guided biopsy (PFB) in men with MRI-visible lesions. MATERIALS AND METHODS: We retrospectively analyzed 1119 men who underwent MB (n = 767) or PFB (n = 352) between 2019 and 2022. Inverse probability of treatment weighting based on a logistic regression propensity score was applied to balance baseline characteristics between groups. Weighted logistic regression models were used to compare csPCa detection in the overall cohort and in subgroups of biopsy-naïve men and those with anterior lesions. A separate multivariable logistic regression was performed in the full cohort to identify independent predictors of csPCa. Concordance between biopsy and radical prostatectomy Gleason scores was also evaluated. RESULTS: = .01). CONCLUSIONS: While MB demonstrated higher csPCa detection in adjusted analyses, the benefit was not consistent across all settings. Further studies are warranted to determine whether this reflects a methodological advantage or context-dependent factors.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".