Implementing evidence‐based strategies for men with biochemically recurrent and advanced prostate cancer: Consensus recommendations from the US Prostate Cancer Conference 2024
Bibliographic record
Abstract
Current US clinical practice guidelines for advanced prostate cancer management contain recommendations based on high-level evidence from randomized controlled trials; however, these guidelines do not address the nuanced clinical questions that are unanswered by prospective trials but nonetheless encountered in day-to-day practice. To address these practical questions, the 2024 US Prostate Cancer Conference (USPCC 2024) was created to generate US-focused expert clinical decision-making guidance for circumstances in which level 1 evidence is lacking. At the second annual USPCC meeting (USPCC 2024), a multidisciplinary panel of experts convened to discuss ongoing clinical challenges related to 5 topic areas: biochemical recurrence; metastatic, castration-sensitive prostate cancer; poly [ADP-ribose] polymerase inhibitors; prostate-specific membrane antigen radioligand therapy; and metastatic, castration-resistant prostate cancer. Through a modified Delphi process, 34 consensus recommendations were developed and are intended to provide clinicians who manage prostate cancer with guidance related to the implementation of novel treatments and technologies. In this report, the authors review the areas of consensus identified by the USPCC 2024 experts and evaluate ongoing unmet needs regarding translational application of the current clinical evidence.
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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 | 0.221 | 0.292 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.009 |
| Bibliometrics | 0.014 | 0.006 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.010 | 0.014 |
| Research integrity | 0.014 | 0.018 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".