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Record W2916896955 · doi:10.3171/2018.10.focus18445

Infective endocarditis in patients with pyogenic spondylodiscitis: implications for diagnosis and therapy

2019· article· en· W2916896955 on OpenAlexaff
Bedjan Behmanesh, Florian Geßler, Katrin Schnoes, Daniel Dubinski, Sae‐Yeon Won, Juergen Konczalla, Volker Seifert, Lutz Weise, Matthias Setzer

Bibliographic record

VenueNeurosurgical FOCUS · 2019
Typearticle
Languageen
FieldMedicine
TopicInfective Endocarditis Diagnosis and Management
Canadian institutionsQueen Elizabeth II Health Sciences CentreDalhousie University
Fundersnot available
KeywordsMedicineSpondylodiscitisInfective endocarditisEndocarditisIncidence (geometry)SurgeryMortality rateHeart failureDiscitisInternal medicineRadiologyMagnetic resonance imaging

Abstract

fetched live from OpenAlex

OBJECTIVEThe incidence of patients with pyogenic spinal infection is increasing. In addition to treatment of the spinal infection, early diagnosis of and therapy for coexisting infections, especially infective endocarditis (IE), is an important issue. The aim of this study was to evaluate the proportion of coexisting IE and the value of routine transesophageal echocardiography (TEE) in the management of these patients.METHODSThe medical history, laboratory data, radiographic findings, treatment modalities, and results of TEE of patients admitted between 2007 and 2017 were analyzed.RESULTSDuring the abovementioned period, 110 of 255 total patients underwent TEE for detection of IE. The detection rate of IE between those patients undergoing and not undergoing TEE was 33% and 3%, respectively (p < 0.0001). Thirty-six percent of patients with IE needed cardiac surgical intervention because of severe valve destruction. Chronic renal failure, heart failure, septic condition at admission, and preexisting heart condition were significantly associated with coexisting IE. The mortality rate in patients with IE was significantly higher than in patients without IE (22% vs 3%, p = 0.002).CONCLUSIONSTEE should be performed routinely in all patients with spondylodiscitis.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.559

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)0.0000.000

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 head0.012
GPT teacher head0.256
Teacher spread0.244 · 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

Citations34
Published2019
Admission routes1
Has abstractyes

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