Management of nonmetastatic castration-resistant prostate cancer
Bibliographic record
Abstract
PURPOSE OF REVIEW: The widespread use of prostate-specific antigen (PSA) resulted in stage migration of prostate cancer where androgen deprivation therapy (ADT) is administered for biochemical recurrence in patients following primary treatment. A proportion of these patients progress to a disease state termed nonmetastatic castration-resistant prostate cancer (nmCRPC), with a rising PSA despite ADT and without evidence of metastases on conventional imaging. We will review the treatment options in nmCRPC, especially in light of recent trials showing significant improvement in metastasis-free survival with newer agents. RECENT FINDINGS: Historically, nmCRPC patients were followed-up if PSA doubling-time (PSADT) exceeded 10 months. Treatment options for patients with shorter PSADT included hormonal manipulations that often resulted in transient PSA decline. Denosumab was found to delay the onset of bone metastasis but did not impact survival. Recently, phase 3 trials showed that second-generation antiandrogens resulted in a significant delay in metastasis and a trend toward survival improvement in a select group of nmCRPC patients. SUMMARY: The importance of reducing mortality and morbidity associated with metastasis has led to the acceptance of new primary endpoints in the design of trials for nmCRPC and might result in widespread approval of new agents for this disease state.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".