Current trends and challenges in infective endocarditis
Bibliographic record
Abstract
PURPOSE OF REVIEW: Infective endocarditis (IE) is a complex disease with increasing global incidence. This review explores recent trends in IE infection patterns, including healthcare-associated IE (HAIE), drug-use-associated IE (DUA-IE), multidrug-resistant organisms (MDROs), and challenges in managing prosthetic valve and device-related infections. RECENT FINDINGS: Staphylococcus aureus has emerged as the leading cause of IE, especially in HAIE and DUA-IE cases. Increasingly prevalent MDROs, such as methicillin-resistant Staphylococcus aureus and vancomycin-resistant enterococci, pose further clinical challenges. Advances in molecular diagnostics have improved the detection of culture-negative endocarditis. The introduction of the AngioVAC percutaneous aspiration device promises to change the management of right and possibly some left sided IE. Multidisciplinary team management and early surgery have demonstrated improved outcomes including partnerships with psychiatry and addictions services for those with intravenous DUA-IE. SUMMARY: IE presents significant diagnostic and therapeutic challenges due to evolving infection patterns, MDROs, and HAIE. Early diagnosis using advanced imaging, appropriate early antimicrobial therapy, and multidisciplinary care, including timely surgery, are critical for optimizing patient outcomes.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".