Left ventricular ejection fraction assessment by non-cardiologists from transverse views using a simplified wall motion score index
Bibliographic record
Abstract
For the non-cardiologist emergency physician and intensivist, performing an accurate estimation of left ventricular ejection fraction (LVEF) is essential for the management of critically ill patients, such as patients presenting with shock, severe respiratory distress or chest pain. Our objective was to develop a semi-quantitative method to improve visual LVEF evaluation. A group of 12 sets of transthoracic echocardiograms with LVEF in the range of 18-64% were interpreted by 17 experienced observers (PRO) and 103 untrained observers or novices (NOV), without previous training in echocardiography. They were asked to assess LVEF by two different methods: i) visual estimation (VIS) by analysing the three classical left ventricle (LV) short-axis views (basal, midventricular and apical short-axis LV section) and ii) semi-quantitative evaluation (base, mid and apex (BMA)) of the same three short-axis views. The results for each of these two methods for both groups (PRO and NOV) were compared with LVEF obtained by radionuclide angiography. The semi-quantitative method (BMA) improved estimation of LVEF by PRO for moderate LV dysfunction (LVEF 30-49%) and normal LVEF. The visual estimate was better for lower LVEF (<30%). In the NOV group, the semi-quantitative method was better than than the visual one in the normal group and in half of the subjects in the moderate LV dysfunction (LVEF 30-49%) group. The visual estimate was better for the lower LVEF (ejection fraction <30%) group. In conclusion, semi-quantitative evaluation of LVEF gives an overall better assessment than VIS for PRO and untrained observers.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".