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Record W2931133684 · doi:10.12968/coan.2019.24.4.212

Canine infective endocarditis

2019· article· en· W2931133684 on OpenAlexaboutno aff
Nora Romero‐Fernández, Valentina Palermo

Bibliographic record

VenueCompanion animal · 2019
Typearticle
Languageen
FieldMedicine
TopicInfective Endocarditis Diagnosis and Management
Canadian institutionsnot available
Fundersnot available
KeywordsInfective endocarditisEndocardiumEndocarditisBartonellaMedicineSepsisMicrobiologyBlood cultureBiologyAntibioticsImmunologyInternal medicine

Abstract

fetched live from OpenAlex

Canine infective endocarditis is defined as an infection of the endocardium, commonly involving one or more heart valves and leading to proliferative (vegetative) or erosive lesions. Prerequisites for development of infective endocarditis include endocardial damage, formation of a sterile vegetative lesion or coagulum, presence of bacteraemia and microorganism adherence to the coagulum. Predisposing factors include any that may facilitate meeting the prerequisites, including congenital heart disease, extra-oral infections and immunosuppression, amongst others. Large, male, middle-aged and purebred dogs may be overrepresented, in particular Labrador Retrievers, Golden Retrievers, Boxers and German Shepherd Dogs. Typically caused by bacteria, the most common isolates include Streptococcus spp., Staphylococcus spp., Gram-negative rods (particularly Escherichia coli) and Bartonella spp. Blood culture can have low sensitivity, with up to 70% of cultures being negative. Bartonella spp. are increasingly being recognised as a cause of culture-negative aortic infective endocarditis. The preferred method of diagnosis in vivo is echocardiography, with a reported sensitivity of 87.5%. The prognosis is guarded despite appropriate treatment, and some negative prognostic factors have been identified.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.208
Threshold uncertainty score0.999

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)0.0020.002

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 head0.010
GPT teacher head0.265
Teacher spread0.254 · 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; both teacher heads agree on what is shown here.

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

Citations3
Published2019
Admission routes1
Has abstractyes

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