Focal Therapy in Prostate Cancer—Report from a Consensus Panel
Bibliographic record
Abstract
Purpose: To establish a consensus in relation to case selection, conduct of therapy, and outcomes that are associated with focal therapy for men with localized prostate cancer. Material and Methods: Urologic surgeons, radiation oncologists, radiologists, and histopathologists from North America and Europe participated in a consensus workshop on focal therapy for prostate cancer. The consensus process was face to face within a structured meeting, in which pertinent clinical issues were raised, discussed, and agreement sought. Where no agreement was possible, this was acknowledged, and the nature of the disagreement noted. Results: Candidates for focal treatment should have unilateral low- to intermediate-risk disease with clinical stage ≤cT2a. Prostate size and both tumor volume and tumor topography are important case selection criteria that depend on the ablative technology used. Currently, the best method to ascertain the key characteristics for men who are considering focal therapy is exposure to transperineal template mapping biopsies. MRI of the prostate using novel techniques such as dynamic contrast enhancement and diffusion weighed imaging are increasingly being used to diagnose and stage primary prostate cancer with excellent results. For general use, however, these new techniques require validation in prospective clinical trials. Until such are performed, MRI will, in most centers, continue to be an investigative tool in assessing eligibility of patients for focal therapy. Conclusions: Consensus was derived for most of the key aspects of case selection, conduct of treatment, and outcome measures for men who are undergoing focal therapy for localized prostate cancer. The level of agreement achieved will pave the way for future collaborative trials.
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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 | 0.154 | 0.163 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".