Endocarditis in the setting of IDU
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".