Rural Versus Urban Genitourinary Cancer Incidence and Mortality in Pennsylvania: 1990–2019
Bibliographic record
Abstract
Our aim was to describe the incidence and mortality of genitourinary (GU) cancers in rural and urban Pennsylvania counties. We calculated age-adjusted incidence and mortality rates of GU (prostate, bladder, and kidney) cancers from 1990 to 2019 in the Pennsylvania Cancer Registry. We defined rurality using the Center for Rural Pennsylvania’s population density-based definition. We modeled average annual percent changes (AAPC) in age-adjusted incidence and mortality rates using joinpoint regression. Overall GU cancer incidence decreased in rural and urban counties (AAPC −7.5%, p = 0.04 and AAPC −6.6%, p = 0.02, respectively). Prostate cancer incidence decreased in rural and urban counties by −10.5% (p = 0.02) and −9.1% (p = 0.01), respectively. Kidney cancer incidence increased in both rural and urban counties, respectively (AAPC = +11.2, p = 0.002 and +9.3%, p = 0.01). GU cancer mortality decreased in rural and urban counties (AAPC = −11.6, p = 0.047 and AAPC −12.2, p = 0.01, respectively). Prostate cancer mortality decreased at similar rates in rural and urban counties (AAPC −15.5, p = 0.03 and −15.4, p = 0.02, respectively). Kidney cancer mortality decreased in urban (AAPC −6.9% p = 0.03) but remained stable in rural counties. Bladder cancer incidence and mortality were unchanged in both types of counties. Over three decades, GU cancer incidence and mortality decreased across Pennsylvania counties.
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".