Evaluate the Sensitivity and Specificity Echocardiography in Trans-Doppler and Tissue Doppler Method in the Estimation of Left Ventricular End-Diastolic Pressure
Bibliographic record
Abstract
BACKGROUND: Non-invasive survey of left ventricular end-diastolic pressure (LVEDP) by transmitral Doppler echocardiography and tissue Doppler imaging carries important information about left ventricular diastolic function in chosen subsets of patients. This study is planned to assess whether mitral annular velocities (lateral annulus) as assessed by tissue Doppler imaging and transmitral Doppler echocardiography are associated with invasive measures of left ventricular end diastolic pressure and also the estimation of sensitivity and specificity of these methods. METHODS: One hundred ten consecutive patients admitted to cardiac catheterization underwent simultaneous Doppler interrogation measurements of left ventricular pressure were obtained with fluid-filled pressure. The E/Ea ratio associated well with LVEDP (P<0.005 r=0.4) and the correlation more marked in the patients with reduced contractile function. This correlation was independent of gender. RESULTS: The E/Ea ration of <8 best discriminated elevated (LVEDP>12) from normal LVEDP with a sensitivity of 73.5% and specificity 57.8%, PPV and NPV were 75.75% and 55% respectively. Our study results also showed that quantitative estimation of LVEDP could be suggested by the equation of LVEDP=1.2 E/Ea+6.67 ± 8 mmHg P<0.005 B=0.4. Male-LVEDP=0.9 E/Ea + 7.78 ± 7.67 mmHg (r=0.4 Pa<0.005) EF ≥ 50 % -+LVEDP=1.48 E/Ea + 9.05 ± 5.23 (r=0.4 P<0.05) EF<50% -+LVEDP=0.76 E/Ea + 8.4 ± 2.3 (r=0.5 P<0.005)
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".