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Record W2774897565 · doi:10.1097/hco.0000000000000493

Endocarditis in the setting of IDU

2017· article· en· W2774897565 on OpenAlexafffund
Bobby Yanagawa, Anees Bahji, Wiplove Lamba, Darrell H. S. Tan, Asim N. Cheema, Ishba Syed, Subodh Verma

Bibliographic record

VenueCurrent Opinion in Cardiology · 2017
Typearticle
Languageen
FieldMedicine
TopicInfective Endocarditis Diagnosis and Management
Canadian institutionsQueen's UniversityUniversity of TorontoSt. Michael's Hospital
FundersCanadian Institutes of Health Research
KeywordsMedicineInfective endocarditisEndocarditisIntensive care medicineMultidisciplinary approachCohortPopulationPsychiatrySurgeryInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: The purpose of this article is to provide a brief overview of the medical and surgical management of infective endocarditis secondary to IDU, with a focus on the underlying substance use disorder. RECENT FINDINGS: Patients with infective endocarditis secondary to IDU are often young with unique comorbidities including mental illness, chronic hepatitis C, HIV infection, which are often compounded by limited social and familial supports. The focus of management has been treatment of endocarditis using IV antibiotics alongside surgery. Surgical outcomes compare favorably with those of infective endocarditis in the general population but long-term outcomes of IDUs are significantly worse. This is primarily due to the high rate of recidivism of drug use and the risk of prosthetic valve infective endocarditis. Contemporary management of addiction utilizes an integrative approach, combining both pharmacologic and nonpharmacologic strategies while remaining patient-centered. Given the complexity of care required, we advocate for a multidisciplinary team-based approach including psychiatry, infectious disease, cardiology, cardiac surgery and social services. SUMMARY: Infective endocarditis secondary to IDU remains a medical and surgical challenge with dismal outcomes. Here we offer practical suggestions on the multidisciplinary management of this challenging and high-risk patient cohort.

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 score0.121
Threshold uncertainty score0.316

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.083
GPT teacher head0.410
Teacher spread0.327 · 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

Citations35
Published2017
Admission routes2
Has abstractyes

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