An Early Diagnosis of Endocarditis Facilitated by the Electronic Stethoscope
Bibliographic record
Abstract
The practice of cardiac auscultation is a critical tool used by physicians to detect alterations in the cardiovascular system. A case of both left and right sided endocarditis initially detected by electronic auscultation in a woman with a history of injection drug use is described. The electronic stethoscope, with the ability to amplify heart sounds, established the presence of both a systolic and diastolic murmur when standard auscultation failed to detect the diastolic component. Urgent standard echocardiography confirmed concurrent tricuspid and aortic valves endocarditis, and the patient was referred for surgical evaluation urgently. The present case demonstrates the value of the electronic stethoscope to amplify murmurs in the early detection of endocarditis. The case presented also serves as a useful reminder that right-sided endocarditis can have important leftsided complications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".