Pyelonephritis and Bacteremia From Lactobacillus acidophilus
Bibliographic record
Abstract
Lactobacillus species have been uncommonly reported to cause clinically significant infections in both immunocompetent and immunocompromised hosts. Lactobacillus bacteremia of renal origin remains a rare clinical entity. We describe a case of pyelonephritis and bacteremia from Lactobacillus acidophilus . To our knowledge, this is only the fourth reported case of pyelonephritis caused by Lactobacillus . We report the case of a 63 year old African American female who had a history of right sided nephrolithiasis and hydronephrosis with recent right ureteral stent placement. She was admitted with pyelonephritis and the ureteral stent was subsequently removed. A new right ureteral mass was discovered at the time of stent removal and biopsy revealed high grade urothelial carcinoma. Blood and urine cultures grew Lactobacillus acidophilus . Antibiotic therapy was directed according to susceptibility pattern. Lactobacillus should be considered as a pathogen when it is isolated (in the appropriate clinical setting) from at least two blood cultures or in one blood culture along with another source especially when the host has one or more predisposing conditions. Lactobacillus species are associated with unusual antimicrobial susceptibility patterns. Lactobacillus should be a suspected pathogen in patients with bacteremia due to gram-positive rods whose condition does not improve with vancomycin therapy. doi:10.4021/jmc638e
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".