Trends in Prostate Cancer Incidence and Survival by Gleason Score from 2000 to 2020: A Population-Based Study in Northeastern Italy
Bibliographic record
Abstract
BACKGROUND: Prostate cancer (PCa) trends have evolved due to changing screening practices. This study assessed long-term trends in PCa incidence and survival according to Gleason score (GS) in Friuli Venezia Giulia, northeastern Italy. METHODS: A population-based study was conducted, encompassing 21,571 PCa cases from the regional Cancer Registry, diagnosed between 2000 and 2020. Age-standardized incidence rates and 5-year overall (OS) and net survival (NS) were assessed by GS (2-6, 7, 8-10) and age group (<65, 65-74, ≥75). Trends were analyzed using Joinpoint regression. RESULTS: PCa incidence increased from 2000 to 2007 (Annual Percent Change, APC = +1.8%), then declined sharply until 2010 (APC = -7.6%) and remained stable thereafter. Incidence of low-grade cancers (GS 2-6) decreased across all age groups, especially in men aged ≥ 75 years (APC = -8.1%). The incidence of GS 7 rose until 2007 and then stabilized. High-grade cancers (GS 8-10) showed a stable incidence, but their proportion increased from 20% to 29%, mainly in older men. Survival improved across all GS groups. For GS 2-6, OS increased from 81.4% to 88.2%; for GS 7, from 78.1% to 88.1%. GS 8-10 had smaller gains, but NS reached 82% in recent years. Among men aged ≥ 75 years, OS for GS 7 rose from 51.9% to 78.1%, and for GS 8-10, from 43.9% to 54.4%. NS remained high for GS ≤ 7. CONCLUSIONS: While overall outcomes improved, the increasing proportion of high-grade PCa, despite a stable incidence, raises concerns, particularly in older men, and calls for tailored clinical strategies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".