Two-dimensional echocardiography estimation of right ventricular ejection fraction by wall motion score index.
Bibliographic record
Abstract
BACKGROUND: Radionuclide angiography (RNA) and echocardiography (biplane Simpson method) are the most accepted methods for right ventricular ejection fraction (RVEF) evaluation. The authors tried to determine a new method to measure RVEF based on wall motion score index (WMSI). OBJECTIVES: One hundred forty-one patients with RV dysfunction had transthoracic echocardiography (TTE) evaluation of the right ventricle. In a first group of 54 patients, a correlation between RVEF using the biplane Simpson method (four chamber and two chamber [4C+2C]) and RV-WMSI was established from a polar map using an eight-segment model to find a regression equation. With the second group of 51 subsequent patients, this equation (RVEF=73.07-20.7 x WMSI), comparing the RVEF-WMSI with the biplane Simpson RVEF (4C+2C), was validated. In a third group of 36 consecutive patients with abnormal RV contractility, the RVEF was calculated by RNA and this RVEF was correlated to the RV-WMSI. RESULTS: The first group of 54 patients had a correlation coefficient of 0.84 between WMSI and RVEF (biplane Simpson method). The results from the second group of 51 patients with RVEF derived from the estimated regression equation correlated well with the biplane Simpson RVEF (r=0.84). The correlation coefficient for these two groups taken together (105 patients), that is, between WMSI and RVEF (biplane Simpson method), was 0.92. The third group of 36 patients with RNA-EF displayed a correlation coefficient of 0.83 with RV-WMSI. CONCLUSION: This new semiquantitative method for estimating RVEF from RV-WMSI is easy to use in routine TTE and shows an excellent correlation with the biplane Simpson method and RNA.
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".