Surgical Management of Infective Endocarditis Complicated by Embolic Stroke
Bibliographic record
Abstract
There has been an overall improvement in surgical mortality for patients with infective endocarditis (IE), presumably because of improved diagnosis and management, centered around a more aggressive early surgical approach. Surgery is currently performed in approximately half of all cases of IE. Improved survival in surgery-treated patients is correlated with a reduction in heart failure and the prevention of embolic sequelae. It is reported that between 20% and 40% of patients with IE present with stroke or other neurological conditions. It is for these IE patients that the timing of surgical intervention remains a point of considerable discussion and debate. Despite evidence of improved survival in IE patients with earlier surgical treatment, a significant proportion of patients with IE and preexisting neurological complications either undergo delayed surgery or do not have surgery at all, even when surgery is indicated and guideline endorsed. Physicians and surgeons are caught in a common conundrum where the urgency of the heart operation must be balanced against the real or perceived risks of neurological exacerbation. Recent data suggest that the risk of neurological exacerbation may be lower than previously believed. Current guidelines reflect a shift toward early surgery for such patients, but there continue to be important areas of clinical equipoise. Individualized clinical assessment is of major importance for decision making, and, as such, we emphasize the need for the functioning of an endocarditis team, including cardiac surgeons, cardiologists, infectious diseases specialists, neurologists, neurosurgeons, and interventional neuroradiologists. Here, we present 2 illustrative cases, critically review contemporary data, and offer conceptual and practical suggestions for clinicians to address this important, common, and often fatal cardiac condition.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".