Injection Drug Use Endocarditis: An Inner-City Hospital Experience
Bibliographic record
Abstract
Background There has been a rise in the incidence of injection drug use and associated infective endocarditis. Methods The clinical outcomes of 39 patients admitted with injection drug use–associated infective endocarditis were collected with a mean follow-up of 14 months. The outcomes were compared for patients treated medically with those undergoing surgical intervention. Re sults: The mean age was 39 ± 11 years; 54% were female. Thirty-two patients (82%) had native and 7 (18%) prosthetic infective endocarditis. The tricuspid valve was affected in 17 patients (43%), the mitral in 10 (26%), the aortic in 4 (10%), and multiple valves in 8 (20%). Sixteen (41%) patients underwent surgery, and 23 (59%) were treated with medical therapy. The indications for surgery included heart failure, systemic emboli, recurrent infection, and vegetation size ≥10 mm. Patients undergoing surgery had a higher rate of paravalvular abscess (25% vs 0%, P = 0.02), valve perforation (37% vs 11%, P = 0.04), and mitral valve involvement (44% vs 13%, P = 0.06), whereas medically treated patients had higher tricuspid valve involvement (61% vs 19%, P = 0.02). During follow-up, 26% of medical and 31% of surgical cohort patients died ( P = 0.7). Mortality was highest (54%) among those who continued medical management despite an indication for surgery. Univariate predictors of mortality were age (odds ratio [OR] 1.09, 95% confidence interval [CI]: 1.01-1.17; P = 0.02), heart failure (OR 6.9; 95% CI: 1.24-37.49; P = 0.02), septicemia (OR 4.40; 95% CI:0.99-19.54; P = 0.05), and shock (OR 10.8; 95% CI: 1.68-69.92; P = 0.01). Conclusions Despite contemporary therapy, patients with injection drug use–associated infective endocarditis remain at high risk of complications and poor clinical outcomes. These findings highlight the need for developing new care pathways and a team approach for effective management.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".