Management algorithms for prostate-specific antigen progression in prostate cancer: Biochemical recurrence after definitive therapy and progression to non-metastatic castrate-resistant prostate cancer
Bibliographic record
Abstract
INTRODUCTION: Current prostate cancer (PCa) guidelines primarily focus on localized or metastatic PCa. A multidisciplinary genitourinary oncology panel determined that additional guidance focusing on monitoring and management of biochemical recurrence (BCR) following radical therapy and non-metastatic castration-resistant prostate cancer (nmCRPC) was warranted. METHODS: The most up-to-date national and international guidelines, consensus statements, and emerging phase 3 trials were identified and used to inform development of algorithms by a multidisciplinary genitourinary oncology panel outlining optimal monitoring and treatment for patients with non-metastatic PCa. RESULTS: A total of eight major national and international guidelines/consensus statements published since 2015 and three phase 3 trials were identified. Working group discussions among the multidisciplinary genitourinary oncology panel led to the development of two algorithms: the first addressing management of patients with BCR following radical therapy (post-BCR), and the second addressing management of nmCRPC. The post-BCR algorithm suggests consideration of early salvage treatment in select patients and provides guidance regarding observation vs. intermittent or continuous androgen-deprivation therapy (ADT). The nmCRPC algorithm suggests continued ADT and monitoring for all patients, with consideration of treatment with apalutamide or enzalutamide for patients with high-risk disease (prostate-specific antigen [PSA] doubling time of ≤ 10 months). CONCLUSIONS: Two treatment algorithms have been developed to guide the management of non-metastatic PCa and should be considered in the context of local guidelines and practice patterns.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".