Multiparametric MRI fusion-guided biopsy for the diagnosis of prostate cancer
Bibliographic record
Abstract
PURPOSE OF REVIEW: To discuss the timing, benefits, limitations and current controversies of multiparametric magnet resonance imaging (mpMRI) combined with fusion-guided biopsy and consider how additional incorporation of multivariable risk stratification might further improve prostate cancer diagnosis. RECENT FINDINGS: MpMRI has been proven advantageous over standard practice for biopsy-naïve men and men with previous biopsy in large prospective studies providing level 1b evidence. Upfront multivariable risk stratification followed by or combined with mpMRI further improves diagnostic accuracy. Regarding active surveillance, mpMRI in combination with fusion biopsy can support initial candidate selection and may help to monitor disease progression. mpMRI and fusion biopsy, however, do not spare failure and conflicting data exists to what extend (systematic) biopsies can be omitted. SUMMARY: Integration of mpMRI into the diagnostic pathway for prostate cancer is beneficial; yet more prospective and randomized data is needed to establish reliable procedure standards after mpMRI acquisition.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".