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Record W4387014112 · doi:10.1099/acmi.0.000525.v3

Yersinia pseudotuberculosis bacteraemia with splenic abscesses: a case report

2023· article· en· W4387014112 on OpenAlexaff
Rahel T Zewude, Aleksandra Stefanovic, Zersenay Alem

Bibliographic record

VenueAccess Microbiology · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicYersinia bacterium, plague, ectoparasites research
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreSt. Paul's HospitalUniversity of British ColumbiaProvidence Health CareUniversity of Toronto
Fundersnot available
KeywordsYersinia pseudotuberculosisLeukocytosisMedicineCorynebacterium pseudotuberculosisGastroenterologyInternal medicineSeptic shockDifferential diagnosisCeftriaxonePathologyChronic liver diseaseAscitesCirrhosisMicrobiologySepsisAntibioticsBiologyBacteria

Abstract

fetched live from OpenAlex

Introduction. Yersinia pseudotuberculosis has been known to cause a variety of clinical manifestations ranging from mild enteric illness to bacteraemia with septic shock and extraintestinal abscesses. Patients with liver disease and iron overload are at risk of more severe disease manifestations. Case Report. A middle-aged male with chronic alcohol use disorder presented with confusion and jaundice, with ascites and asterixis noted on examination. His blood work was remarkable for neutrophilic leukocytosis, elevated liver enzymes and lactate. An abdominal computed tomography scan revealed splenic microabscesses and a cirrhotic liver. Yersinia pseudotuberculosis was recovered from his blood cultures and he was treated with ceftriaxone following susceptibility results. Conclusion. Y. pseudotuberculosis should be considered in the differential diagnosis of splenic or other extraintestinal microabscesses particularly in patients with chronic liver disease.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0040.003
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0050.003
Science and technology studies0.0050.004
Scholarly communication0.0040.004
Open science0.0020.004
Research integrity0.0100.006
Insufficient payload (model declined to judge)0.0040.002

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 head0.019
GPT teacher head0.320
Teacher spread0.301 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
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

Citations3
Published2023
Admission routes1
Has abstractyes

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