Challenges of Infective Endocarditis in South Africa, a Low- to Middle-Income Country
Bibliographic record
Abstract
Infective endocarditis (IE) remains a complex and life-threatening disease globally, but in low- and middle-income countries (LMICs), the burden is amplified by a confluence of socio-economic disparities, high rates of rheumatic heart disease (RHD), limited local data and systemic healthcare limitations. In South Africa, a representative LMIC, patients with IE are typically younger and often present with advanced disease. This is attributed to delayed diagnosis, limited access to echocardiography, and a high prevalence of blood culture-negative infective endocarditis (BCNIE), which complicates microbiological confirmation and treatment selection compared to patients in high-income countries. Rheumatic heart disease continues to be a dominant predisposing factor, and the burden of long term anticoagulation following valve replacement contributes to recurrent complications. In addition, there has been a shift in epidemiological profile with Staphylococcus aureus predominating. Although international data support oral step-down therapy and early surgical intervention in selected patients, the implementation of these strategies in South Africa and other LMICs are constrained by diagnostic limitations, insufficient surgical infrastructure and health care worker hesitance to implement protocols validated in Western countries. Despite these barriers, growing evidence supports the need for locally adapted guidelines, including screening at primary care level, multidisciplinary endocarditis teams, and prospective local research to evaluate the safety and feasibility of simplified treatment protocols. The ultimate goal should be the development of resource-sensitive interventions with broader applicability across similar settings worldwide, ultimately aiming to reduce the global clinical and economic burden of IE.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".