Accuracy of Doppler-echocardiographic parameters for the detection of aortic bileaflet mechanical prosthetic valve dysfunction
Bibliographic record
Abstract
AIMS: In vitro and in vivo studies were performed to evaluate the diagnostic accuracy of the different Doppler-echocardiographic parameters proposed in the American Society of Echocardiography guidelines to identify dysfunction of bileaflet mechanical valves (BMV) in the aortic position. METHODS AND RESULTS: Two models of BMV (St Jude HP, MCRI On-X) of different sizes (21;23;25;27 mm) were tested in vitro under a wide range of cardiac outputs (3-7 L/min). The motion of one or both leaflets was restricted to induce a mild (25% restriction in total valve orifice area) and moderate-to-severe (50% restriction in total valve area). Doppler-echocardiographic parameters of valve function were also measured in 17 patients with BMV of whom 4 had valve dysfunction confirmed by cinefluoroscopy. The specificity of all the parameters was high (in vitro: 83-100%; in vivo: 69-100%), but the sensitivity was low (range: 0-83% and 25-100%, respectively). A higher cut-off value for the ratio of peak left ventricular outflow tract velocity to peak aortic velocity or Doppler velocity index (DVI) (<0.35 instead of 0.3 or 0.25) improved the sensitivity (>90%) for the detection of moderate-to-severe dysfunction but remained low for mild dysfunction (50%). Furthermore, a difference of normal reference effective orifice area (EOA) minus measured EOA (EOA-D) >1 standard deviation identified mild and moderate-to-severe dysfunction with sensitivity of 61 and 100%, respectively. CONCLUSION: The Doppler-echocardiographic parameters and criteria proposed in the guidelines lack sensitivity for the detection of BMV dysfunction. The utilization of a DVI < 0.35 or an EOA-D > 1 SD improved the sensitivity (>90%) for the detection of moderate-to-severe dysfunction, but the sensitivity remained suboptimal (<65%) for detection of mild dysfunction.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.009 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".