Bladder cancer stage and mortality: urban vs. rural residency
Bibliographic record
Abstract
OBJECTIVE: Relative to urban populations, rural patients may have more limited access to care, which may undermine timely bladder cancer (BCa) diagnosis and even survival. METHODS: We tested the effect of residency status (rural areas [RA < 2500 inhabitants] vs. urban clusters [UC ≥ 2500 inhabitants] vs. urbanized areas [UA, ≥50,000 inhabitants]) on BCa stage at presentation, as well as on cancer-specific mortality (CSM) and other cause mortality (OCM), according to the US Census Bureau definition. Multivariate competing risks regression (CRR) models were fitted after matching of RA or UC with UA in stage-stratified analyses. RESULTS: Of 222,330 patients, 3496 (1.6%) resided in RA, 25,462 (11.5%) in UC and 193,372 (87%) in UA. Age, tumor stage, radical cystectomy rates or chemotherapy use were comparable between RA, UC and UA (all p > 0.05). At 10 years, RA was associated with highest OCM followed by UC and UA (30.9% vs. 27.7% vs. 25.6%, p < 0.01). Similarly, CSM was also marginally higher in RA or UC vs. UA (20.0% vs. 20.1% vs. 18.8%, p = 0.01). In stage-stratified, fully matched CRR analyses, increased OCM and CSM only applied to stage T1 BCa patients. CONCLUSION: We did not observe meaningful differences in access to treatment or stage distribution, according to residency status. However, RA and to a lesser extent UC residency status, were associated with higher OCM and marginally higher CSM in T1N0M0 patients. This observation should be further validated or refuted in additional epidemiological investigations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".