Cardiac Point of Care Ultrasound (POCUS) Used to Diagnose Infective Endocarditis Following Multiple Negative Echocardiograms
Bibliographic record
Abstract
Infective endocarditis (IE) is a life-threatening condition often diagnosed using the modified Duke’s criteria, including bacteremia and pathognomonic echocardiographic findings. However, up to 30% of cases yield inconclusive results with transthoracic echocardiograms (TTE) or transesophageal echocardiograms (TEE). We present a case of a 68-year-old man with methicillin-susceptible Staphylococcus aureus (MSSA) bacteremia and recurrent fevers, in which multiple echocardiograms failed to detect valvular vegetations. However, an advanced cardiac point of care ultrasound (POCUS) examination identified a vegetation on the aortic valve, later confirmed by TTE and TEE. Although generalization is limited due to operator expertise and patient characteristics, this case demonstrates the utility of advanced cardiac POCUS in diagnosing IE in critically ill patients with negative initial echocardiograms. Incorporating advanced cardiac POCUS into routine diagnostic workflows may improve diagnostic accuracy and patient outcomes. Increasing use of advanced cardiac POCUS also highlights the importance of expanding proficiency among intensivists.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".