Incidence and mortality trends of metastatic prostate cancer: Surveillance, Epidemiology, and End Results database analysis
Bibliographic record
Abstract
INTRODUCTION: In the past decade, prostate cancer screening decreased, raising the concern of delays in diagnosis and leading to an increase in new cases of metastatic prostate cancer. This study evaluated whether these changes may have impacted trends in metastatic prostate cancer incidence and survival. METHODS: Metastatic prostate cancer diagnoses from 2008-2016 were identified from the Surveillance, Epidemiology, and End Results (SEER) 18 registries. Age-adjusted incidence rates per 100 000 were calculated by time periods and demographic variables. Two-year all-cause and prostate cancer-specific mortality were calculated for patients diagnosed from 2008-2014, and multivariable Cox proportional hazards models were used to evaluate the impact of demographic and clinical variables. RESULTS: Incidence rates of metastatic prostate cancer increased by 18% from 2008-2009 to 2014-2016 (incidence rate ratio [IRR]=1.18, 95% confidence interval [CI] 1.14-1.21). This trend was observed across multiple subgroups but was greatest in non-Hispanic Whites and patients living in counties 0-10% below poverty level. There was an overall decreased risk of all-cause and prostate cancer-specific mortality, but unmarried men and men living in counties >20% below poverty level showed statistically significant increased risk of prostate cancer-specific mortality. CONCLUSIONS: Non-Hispanic Whites and the wealthiest subgroups had the largest increase in incidence of metastatic prostate cancer since 2008. Despite trends of decreased risk of prostate cancer-specific mortality, we found certain populations experienced increases in mortality risk. Studies exploring the role of socioeconomic factors on screening and access to newer treatments are needed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".