International prevalence of nonmetastatic (M0) castration-resistant prostate cancer (CRPC).
Bibliographic record
Abstract
e16052 Background: CRPC represents a growing public health concern that has not been adequately quantified. In the absence of metastases, CRPC represents a transitional disease state defined by increases in prostate specific antigen (PSA) despite castration levels of androgens during androgen deprivation therapy (ADT). We developed a model to estimate M0 CRPC prevalence in selected countries. Methods: A patient-flow model was developed to estimate 5-year limited duration prevalence of prostate cancer (PC) and M0 PC in each country, each year (2008 to 2028) using age-specific incidence and survival data from population cancer registries from 28 countries in North (US, Canada, Mexico) and South America (Brazil), Europe (18 countries), Asia (5 countries), and Australia. The proportion of men with M0 PC treated with ADT was based on literature reports and survey research with physicians treating PC. PSA relapse rates from published literature were used to estimate CRPC among ADT treated. Results: PC prevalence is driven by size and aging of populations, with notable increases predicted within 15 years in regions other than US, Canada, and EU5 (table). The prevalence model, which utilized country-level data, indicates that M0 CRPC represents a relatively small proportion (2-8%) of the total PC population. Nonetheless, widespread screening and demographic changes dictate that the prevalence of CRPC will increase over the next 15 years, assuming current ADT rates. Conclusions: Incidence trends in PC are dependent on PSA screening, which is projected to lead to an increase in M0 PC and PC prevalence in most regions. The number of men treated with ADT and who ultimately develop CRPC will increase accordingly, as demonstrated in the model. [Table: see text]
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".