Pyelonephritis and Bacteremia Caused by Klebsiella variicola following Renal Transplantation
Bibliographic record
Abstract
Klebsiella variicola (K. variicola) is a Gram-negative organism genetically similar to Klebsiella pneumoniae (K. pneumoniae) that can cause a variety of diseases in humans. Bacteremia due to K. variicola is associated with a higher mortality rate than bacteremia with K. pneumoniae. Here, we describe a 65-year-old woman who developed pyelonephritis 2 months after receiving a renal transplantation following a longstanding history of end-stage renal disease secondary to polycystic kidney disease. Her creatinine on admission was unchanged from her posttransplant baseline, and an abdominal CT scan showed inflammatory changes around the transplanted kidney that were suggestive of an infection rather than allograft rejection. She was initially treated empirically with meropenem given a history of extended-spectrum beta-lactamase- (ESBL-) producing E. coli bacteriuria. After a day of therapy with meropenem, her therapy was streamlined based on culture results to ceftriaxone. She continued to improve, her kidney function remained stable, and she was prescribed oral ciprofloxacin to complete a 14-day total course of antibiotics. This case is the first reported instance of K. variicola bacteremia associated with pyelonephritis in a renal transplant recipient. Hospitalization with acute pyelonephritis within the first year following kidney transplant is common and is associated with increased risk of graft loss and mortality. However, K. variicola is not a commonly known organism to cause this infection. Despite the risk of allograft failure in this circumstance, this patient was successfully treated with a 14-day course of antibiotic 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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".