Prevalence and natural history of non-metastatic castrate resistant prostate cancer: A population-based analysis.
Bibliographic record
Abstract
65 Background: The natural history of non-metastatic castrate resistant prostate cancer (nmCRPC) prior to the introduction of novel anti-androgen agents in a real-world setting is largely unknown. Methods: This was a retrospective population-based cohort study of men with nmCRPC in Ontario, Canada. Patients with a diagnosis of prostate cancer, castrate level of testosterone ( < 1.7nmol/L) and a PSA > 2.0nmol/L with a subsequent rise > 25% from the nadir, and without diagnostic or treatment codes for metastasis were included. Annual prevalence of nmCRPC was calculated. Crude time from nmCRPC to metastasis and death are presented as medians with interquartile range (IQR). Predictors of time from nmCRPC to prostate cancer death were compared using univariable and multivariable Fine and Gray subdistributional hazard models to account for the competing risk of non-prostate cancer death. Results: From January 2007 until March 2018, we identified 2045 patients with nmCRPC. Median age was 79 years (IQR: 72-84). A total of 984 patients (48.1%) received upfront hormonal therapy while 584 (25.8%) received initial radiotherapy (RT) and 478 (23.4%) underwent radical prostatectomy. Median time from primary treatment to nmCRPC was 6 years (IQR: 3-10). PSA at the time of meeting nmCRPC criteria was a median of 3.0 ng/L. Patients were followed for a median 31.1 months (IQR: 19.8-47.9). The overall annual prevalence of nmCRPC ranged from 1,519-1,913 patients, representing 7-12% of men with prostate cancer prescribed androgen deprivation therapy each year. Crude median time from nmCRPC to all-cause death was 37.6 months (IQR: 22.1-55.4). Median time from nmCRPC to metastasis and metastasis to all-cause death was 20.0 and 8.3 months, respectively. On regression analysis, older age, ADT use with primary treatment, higher PSA at the time of meeting nmCRPC criteria, and grade group predicted time from nmCRPC to prostate cancer death. Conclusions: This is the largest analysis of the prevalence and natural history of nmCRPC. The current study can be used as a historical cohort to compare how novel imaging modalities and advancements in systemic therapy for patients with nmCRPC impact prevalence estimates and disease trajectory over time.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".