Bacillus Calmette-Guerin vaccine and bladder cancer incidence: Scoping literature review and preliminary analysis
Bibliographic record
Abstract
BACKGROUND: The Bacillus Calmette-Guerin (BCG) vaccine has long been used for the prevention of tuberculosis (TB) around the world. BCG is also used as an immunotherapy agent for the treatment of non-muscle invasive urinary bladder cancer. This scoping literature review and preliminary data analysis aims to summarize the literature correlating infantile BCG vaccination with the incidence of future bladder cancer. METHODS: Studies were identified by a formal literature search of MEDLINE and Cochrane Central Registrar of Controlled Trials following PRISMA guidelines. Preliminary data analysis was conducted on publicly accessible data summarizing the impact of gender, BCG vaccination, and socio-economic effects on crude and age-standardized rates of bladder cancer. RESULTS: As part of our analysis, preliminary regression models demonstrated BCG vaccination status, gender, and socio-economic status to have statistically significant effects on crude and age-standardized rates of bladder cancer incidence. BCG vaccination was associated with a 35-37% lower age-standardized rate of bladder cancer incidence. CONCLUSIONS: There is very little literature examining the relationship between prior BCG vaccination and rates of bladder cancer incidence. Our limited data analysis indicates that a relationship does exist between infantile BCG vaccination and later bladder cancer development, although extensive future investigation is needed in this area.
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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 | 0.017 | 0.075 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.011 |
| Bibliometrics | 0.032 | 0.034 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".