Diagnostic value of harmonic transthoracic echocardiography in native valve infective endocarditis: comparison with transesophageal echocardiography
Bibliographic record
Abstract
BACKGROUND: Although echocardiography has been incorporated into the diagnostic algorithm of patients with suspected infective endocarditis (IE), systematic usage in clinical practice remains ill defined. To determine the diagnostic accuracy of detecting vegetations using harmonic transthoracic echocardiography (hTTE) as compared to transesophageal echocardiography (TEE) in patients with an intermediate likelihood of native valve IE. METHODS: Between 2004 and 2005, 36 consecutive inpatients with an intermediate likelihood of disease were prospectively evaluated by hTTE and TEE. RESULTS: Of 36 patients (21 males with a mean age of 57 +/- 15 years, range 32 to 86 years), 19 patients had definite IE by TEE. The sensitivity for the detection of vegetations by hTTE was 84%, specificity of 88%, positive predictive value (PPV) of 89% and negative predictive value (NPV) of 82%. The association between hTTE and TTE interpretation for the presence and absence of vegetations were high (kappa = 0.90 and 0.85 respectively). CONCLUSION: In patients with an intermediate likelihood of native valve IE, TTE with harmonic imaging provides diagnostic quality images in the majority of cases, has excellent concordance with TEE and should be recommended as the first line test.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".