World Journal of Urology
Bibliographic record
Abstract
Purpose To define the role of focal laser ablation (FLA) as clinical treatment of prostate cancer (PCa) using the Delphi consensus method.Methods A panel of international experts in the field of focal therapy (FT) in PCa conducted a collaborative consensus project using the Delphi method.Experts were invited to online questionnaires focusing on patient selection and treatment of PCa with FLA during four subsequent rounds.After each round, outcomes were displayed, and questionnaires were modified based on the comments provided by panelists.Results were finalized and discussed during face-to-face meetings.Results Thirty-seven experts agreed to participate, and consensus was achieved on 39/43 topics.Clinically significant PCa (csPCa) was defined as any volume Grade Group 2 [Gleason score (GS) 3+4].Focal therapy was specified as treatment of all csPCa and can be considered primary treatment as an alternative to radical treatment in carefully selected patients.In patients with intermediate-risk PCa (GS 3+4) as well as patients with MRI-visible and biopsy-confirmed local recurrence, FLA is optimal for targeted ablation of a specific magnetic resonance imaging (MRI)-visible focus.However, FLA should not be applied to candidates for active surveillance and close follow-up is required.Suitability for FLA is based on tumor volume, location to vital structures, GS, MRI-visibility, and biopsy confirmation.Conclusion Focal laser ablation is a promising technique for treatment of clinically localized PCa and should ideally be performed within approved clinical trials.So far, only few studies have reported on FLA and further validation with longer follow-up is mandatory before widespread clinical implementation is justified.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.242 | 0.121 |
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".