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Record W2058896319 · doi:10.4021//jmc.v3i4.781

Pyelonephritis and Bacteremia From Lactobacillus acidophilus

2012· article· en· W2058896319 on OpenAlexvenueno aff
Samad Rasul, Faria Farhat, Shweta Ramsahai, Robert Delapenha, Namrata Shah

Bibliographic record

VenueJournal of Medical Cases · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProbiotics and Fermented Foods
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineLactobacillus acidophilusBacteremiaLactobacillusStentMicrobiologyAntibioticsInternal medicineProbioticBacteriaBiology

Abstract

fetched live from OpenAlex

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

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.811
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.028
GPT teacher head0.244
Teacher spread0.217 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2012
Admission routes1
Has abstractyes

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