The Importance of Echocardiography in Physicians' Support of Endocarditis Prophylaxis
Bibliographic record
Abstract
BACKGROUND: Guidelines advocate for antimicrobial prophylaxis for prevention of bacterial endocarditis. Our objective was to explore physicians' perspectives regarding the importance of echocardiography in the evaluation and management of endocarditis prophylaxis. METHODS: A total of 260 cardiologists and 300 family physicians practicing in Ontario, Canada, were surveyed. Our main outcome measures were physician ratings of the importance of a murmur, echocardiogram findings, echocardiogram comments, comorbid conditions, guidelines, patient insistence, and medicolegal concerns on the decision to recommend prophylaxis, as well as the degree of echocardiographically detected mitral valve abnormality required prior to recommending prophylaxis. RESULTS: The survey response rate was 62%. Echocardiographic findings were rated by 81% of physicians as the most important factor influencing the decision to provide prophylaxis. Conversely, only 27% of physicians placed similar importance on clinical findings. Family physicians relied more heavily on echocardiographic than on clinical findings when supporting endocarditis prophylaxis recommendations. On average, prophylaxis was recommended for moderate to severe regurgitation in a normal valve and mild regurgitation in a mildly abnormal structural mitral valve. Physicians who reported greater reliance on echocardiographic findings advocated for prophylaxis at lower echocardiographic thresholds than did those who reported greater reliance on murmur detection for endocarditis prophylaxis decisions. CONCLUSIONS: Physicians strongly support the use of echocardiography in endocarditis prophylaxis decision making. However, the importance of echocardiography relative to other factors varies across physician specialties. Further studies must evaluate the role of echocardiography in the assessment and management of antimicrobial endocarditis prophylaxis to assist in the development of clear clinical guidelines.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".