A BCG success story: From prevention of tuberculosis to optimal bladder cancer treatment
Bibliographic record
Abstract
BCG remains the most important vaccine for tuberculosis 100 years after its first use, and over the past 4 decades it has become the most widely accepted, effective drug used in the treatment of aggressive localized bladder cancer. This review chronicles the narrow path that led to approval and world-wide acceptance of BCG immunotherapy for bladder cancer while immunotherapy trials in other malignancies were abandoned. Six intravesical instillations of 5x10^8 CFU of BCG weekly after bladder tumor resection, first reported in 1976, is superior to resection alone and resection plus intravesical chemotherapy. Maintenance of effective immune stimulation is surprisingly difficult, but 3 weekly treatments 3, 6, and 12, 18, 24, 30 and 36 months after induction produces further significant reduction in tumor recurrence. This 3 week BCG maintenance schedule alone has reduced disease progression and mortality in multicenter randomized clinical trials. In the new age of immuno-oncology patients with many types of cancer now benefit from immunotherapy, but currently these modern agents are prohibitively expensive for most of the world. In contrast, the low cost and therefore low profitability of BCG has resulted in recurrent shortages that threaten both bladder cancer patients and children at risk for tuberculosis and other serious infections. Humanity has greatly benefited from early 20th century science that developed BCG and the benevolence of doctors Calmette and Guerin who put people over profit and widely shared cultures of the vaccine. The 21st century is bringing new immunotherapies and greatly expanding the types of malignancies that can be treated. Recombinant technology is expected to improve both the efficacy and production of BCG, hopefully expanding the availability of BCG and relieving the recurring supply shortage for both vaccination and cancer therapy.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".