Focal Therapy for Prostate Cancer: Evolutionary Parallels to Breast Cancer Treatment
Bibliographic record
Abstract
PURPOSE: Our goal was to review the history of the adoption of focal therapy for breast and prostate cancer and review common barriers to implementation. MATERIALS AND METHODS: A narrative review of the literature was performed of English-language MEDLINE indexed articles of breast-conservation therapy and prostate cancer focal therapy. RESULTS: The introduction of focal therapy in breast cancer began with pioneering case series, and multiple randomized trials were performed prior to widespread adoption. Focal therapy for prostate cancer has just started the process of clinical trials with a single published randomized controlled trial. Commonly cited barriers to the adoption of prostate focal therapy include historical views of Halstedian tumor biology, tumor multifocality, over-detection, limitations in prostate imaging, and trial design end points. CONCLUSIONS: The adoption of breast-conserving therapy evolved over decades and used data from multiple large, randomized, clinical trials. Barriers to the adoption of prostate cancer local therapy are similar to those faced by breast cancer clinical trials. Completion of well-designed trials in prostate cancer focal therapy is essential for its evidence-based adoption.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".