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Record W2978219063 · doi:10.14740/cr940

<i>Escherichia coli</i> Endocarditis Presenting With Septic Shock in an Immunocompetent Female Patient

2019· article· en· W2978219063 on OpenAlexvenueno aff
Christos Davoulos, Maria Lagadinou, Αθανάσιος Μούλιας, Christos Triantos, Nikolaos Koutsogiannis, Markos Marangos, Stelios F. Assimakopoulos

Bibliographic record

VenueCardiology Research · 2019
Typearticle
Languageen
FieldMedicine
TopicInfective Endocarditis Diagnosis and Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineSeptic shockEndocarditisShock (circulatory)CardiologyEscherichia coliInternal medicineSepsisBiology

Abstract

fetched live from OpenAlex

Escherichia coli ( E. coli ) is a rare cause of infective endocarditis, despite being a common cause of bacteremia. E. coli endocarditis affects most frequently immunocompromised elderly women, especially those with diabetes mellitus. We present a case of a 78-year-old female immunocompetent patient, presenting with septic shock and multiple organ dysfunction syndrome. E. coli was isolated in all sets of blood cultures and in urine culture and a contrast-enhanced abdominal computed tomography (CT) scan revealed spleen and left kidney infracts. Transthoracic echocardiography revealed a large (&gt; 15 mm) mobile mass on the atrial side of the posterior mitral valve leaflet. The patient was initially treated with intravenous ceftriaxone and ciprofloxacin for 2 weeks with successful clinical response and clearance of bacteremia, was then subjected to valve replacement (with isolation of E. coli from replaced valve cultures) and continued antibiotic therapy for additional 4 weeks postoperatively. E. coli has emerged in recent years as an important cause of bacteremia, especially in the elderly. In selected patients, as those with persistent Gram-negative bacteremia or severe sepsis/septic shock, echocardiography is of paramount importance for the diagnosis of Gram-negative endocarditis and should be included in our diagnostic algorithm of patient's evaluation. Cardiol Res. 2019;10(5):318-322 doi: https://doi.org/10.14740/cr940

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.714

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.040
GPT teacher head0.338
Teacher spread0.298 · 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.

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

Citations0
Published2019
Admission routes1
Has abstractyes

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