Assessment of the epidemiological trends for prostate cancer using administrative data in Ontario
Bibliographic record
Abstract
INTRODUCTION: Studies have shown fluctuations in prostate cancer (PCa) incidence and prevalence over time and by region. Less is known about the most recent epidemiological trends by PCa disease stage. METHODS: This study was a population-based, sequential, cross-sectional analysis that used administrative health data from Ontario, Canada. After inclusion, patients were classified into non-metastatic (nm) PCa and metastatic (m) PCa. The primary study outcome was a description of temporal trends in the incidence and prevalence of PCa over the study period (2010-2019), stratified by disease state. Crude incidence and prevalence rates were estimated for each year in the study period. RESULTS: Overall, there were 131 718 men living with PCa in 2019. The incident cohort contained 86 123 patients with nmPCa (n=65 691, 76.3%), mPCa (n=8431, 9.8%), or unknown stage (n=12 001, 13.9%). The prevalence increased from 216 to 253 per 10 000 men between 2010 and 2019, respectively. Between 2011 and 2014, overall PCa incidence decreased from 20.9 to 15.4 per 10 000 men, followed by an increase to 18.8 per 10 000 in 2018. The nmPCa incidence rate was considerably higher compared with mPCa and followed a trend similar to the overall incidence. In contrast, the incidence rate for mPCa demonstrated a continuous increase from 1.5 per 10 000 in 2010 to 2.4 per 10 000 in 2018. CONCLUSIONS: The overall prevalence of PCa has risen steadily over the last decade, despite fluctuations in nmPCa incidence. The concurrent rise in mPCa and nmPCa requires further study regarding the burden of localized and systemic treatment.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".