Left ventricular ejection fraction using a simplified wall motion score based on mid-parasternal short axis and apical four-chamber views for non-cardiologists
Bibliographic record
Abstract
BACKGROUND: There is a need for a convenient, yet reliable method to assess left ventricular ejection fraction (LVEF) with point-of-care ultrasound study (POCUS). We aim to validate a novel and simplified wall motion score LVEF based on the analysis of a simplified combination of echocardiographic views. METHODS: In this retrospective study, transthoracic echocardiograms of randomly selected patients were analysed by the standard 16-segments wall motion score index (WMSI) to derive the reference semi-quantitative LVEF. To develop our semi-quantitative simplified-views method, a limited combination of imaging views and only 4 segments per view were tested: (1) A combination of the three parasternal short-axis views (PSAX BASE, MID-, APEX); (2) A combination of the three apical views (apical 2-chamber, 3-chamber and 4-chamber) and (3) A more limited combination of PSAX-MID and apical 4-chamber is called the MID-4CH. Global LVEF is obtained by averaging segmental EF based on contractility (normal = 60%, hypokinesia = 40%, and akinesia = 10%). Accuracy of the novel semi-quantitative simplified-views WMS method compared to the reference WMSI was evaluated using Bland-Altman analysis and correlation was assessed in both emergency physicians and cardiologists. RESULTS: = 0.90) with very good agreement (mean LVEF bias = - 0.2%) and precision (± 3.3%). CONCLUSIONS: Cardiac POCUS by emergency physicians and other non-cardiologists is a decisive therapeutic and prognostic tool. A simplified semi-quantitative WMS method to assess LVEF using the easiest technically achievable combination of mid-parasternal and apical four-chamber views provides a good approximative estimate for both non-cardiologist emergency physicians and cardiologists.
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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 | 0.007 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".