Disseminated<i>Mycobacterium bovis</i>infection post‐kidney transplant following remote intravesical<scp>BCG</scp>therapy for bladder cancer
Bibliographic record
Abstract
Intravesical Bacillus Camlette-Guérin (BCG) is the treatment of choice for non-muscle invasive bladder cancer, and has been used successfully for over 40 years. A rare and potentially fatal complication of intravesical BCG therapy is BCG-induced sepsis. We report a rare case in which a patient with end-stage renal disease secondary to chronic granulomatous interstitial nephritis underwent remote, pre-transplant intravesical BCG treatment for high-grade non-invasive papillary bladder carcinoma. The patient subsequently received a deceased donor kidney transplant 5 years after BCG therapy, with thymoglobulin induction therapy and standard triple maintenance immunosuppression. Two years post-transplant, he developed BCG-induced sepsis confirmed by cultures from urine, blood, and left native kidney biopsy. He died from disseminated BCG-induced sepsis and failure of his renal allograft. This case highlights the potential adverse reactions associated with intravesical BCG therapy that may occur years after bladder cancer therapy is completed, and should heighten physician awareness for BCG-related infections during pre-transplant assessment and post-transplant care of solid organ transplants recipients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".