Non-muscle invasive bladder cancer burden: The impact of BCG shortage and its interaction with epidemiological patient profile—A population cohort study in Brazil.
Bibliographic record
Abstract
e16602 Background: To evaluate the impact of the Bacillus Calmette-Guérin (BCG) shortage on treatment patterns and recurrence rates in non-muscle invasive bladder cancer (NMIBC) over two decades, with a focus on patient and treatment characteristics in a Brazilian population-based cohort. Methods: This retrospective cohort study utilized data from the Fundação Oncocentro de São Paulo (FOSP) database, including 9,319 patients with confirmed NMIBC (stages 0 and I) treated between 2000 and 2022. Inclusion criteria were age ≥18 years and complete demographic, clinical, and treatment data. Factors analyzed included age, educational level, oncological center type, treatment modality, and tumor stage (Ta, T1, or carcinoma in situ). Kaplan-Meier analysis assessed recurrence rates before and during the BCG shortage. Decision tree analysis identified variables associated with reduced BCG use, with statistical tests (Chi-square, Fisher’s exact, and log-rank) performed at a 5% significance level. Results: BCG use decreased significantly after 2012 (p < 0.0001), corresponding with increased use of intravesical chemotherapy and observation. Factors associated with reduced BCG use included age > 70 years (p = 0.005), T1 stage (p = 0.0001), low educational level (p = 0.0001), treatment in public facilities (p = 0.0001), and care at UNACON centers (p = 0.007). Kaplan-Meier analysis revealed no significant difference in recurrence-free survival before and during the shortage (p = 0.430). Decision tree analysis (79% accuracy) highlighted educational level, tumor stage, and oncological center type as primary determinants of BCG access. Patients with high education, T1 stage, and care at CACON centers were highly likely to receive BCG (99%), while those with low education, Ta/Tis stage, and treatment at UNACON centers had reduced access (90%). Conclusions: This study demonstrates that educational level, tumor stage, and oncological center complexity were key determinants of BCG utilization during the shortage. Addressing disparities in treatment access requires targeted interventions and equitable healthcare policies to minimize the ethical and clinical impact of resource constraints.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".