Differences between rural and urban prostate cancer patients
Bibliographic record
Abstract
BACKGROUND: We hypothesized that the residency status (rural area [RA] vs urban clusters [UC] vs urban areas [UA]) affects stage and cancer-specific mortality (CSM) in contemporary newly diagnosed prostate cancer (PCa) patients of all stages, regardless of treatment. METHODS: Newly diagnosed PCa patients with available residency status were abstracted from the Surveillance, Epidemiology, and End Results database (2004-2016). Propensity-score (PS) matching, cumulative incidence plots, multivariate competing-risks regression (CRR) models were used. RESULTS: Of 531,468 PCa patients of all stages, 6653 (1.3%) resided in RA, 50,932 (9.6%) in UC and 473,883 (89.2%) in UA. No statistically significant or clinically meaningful differences in stage at presentation or CSM were recorded. Conversely, 10-year other cause-mortality (OCM) rates were 27.2% vs 23.7% vs 18.9% (p < 0.001) in RA vs UC vs UA patients, respectively. In CRR models, RA (subhazard ratio [SHR] 1.38; p < 0.001) and UC (SHR 1.18; p < 0.001) were independent predictors for higher OCM relative to UA. These differences remained statistically significant in fully PS-adjusted multivariate CRR models. CONCLUSION: RA, and to a lesser extent UC, PCa patients are at higher risk of OCM than UA patients. Higher OCM may indicate shorter life expectancy and should be considered in treatment decision making.
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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 | 0.000 | 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) | 0.003 | 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".