E-Point Septal Separation Accuracy for the Diagnosis of Mild and Severe Reduced Ejection Fraction in Emergency Department Patients
Bibliographic record
Abstract
Introduction: In the Emergency Department (ED), a thorough cardiovascular evaluation cannot be accomplished only with physical examination. E-Point Septal Separation (EPSS) measure through Point-of-Care Ultrasound (POCUS) has been used to evaluate systolic function in echocardiography. We analyzed EPSS for diagnosis of Left Ventricle Ejection Fraction <50% and ≤40% in ED patients. Methods: Retrospective analysis of a convenience sample of patients presenting to ED with chest pain or dyspnea who underwent admission POCUS evaluation by Internal Medicine Specialist unaware of Transthoracic Echocardiogram. Accuracy was assessed with sensitivity, specificity, likelihood ratios (LR) and Receiver operating characteristics (ROC) curve. The best cut off point was calculated using Youden Index. Results: Ninety-six patients were included. Median EPSS and LVEF were 10mm and 41% respectively. Area Under the ROC Curve (AUC-ROC) to diagnose a LVEF <50% was 0.90 (IC95% 0.84-0.97). Youden Index was 0.71 with cut off point EPSS at 9.5mm, performing with a sensitivity of 0.80, a specificity of 0.91, a positive LR of 9.8 and a negative LR of 0.2. AUC-ROC to diagnose a LVEF ≤40% was 0.91 (IC95% 0.85-0.97). Youden Index was 0.71 with a cut off point EPSS at 9.5mm, performing with a sensitivity of 0.91 and specificity of 0.80, a positive LR of 4.7 and a negative LR of 0.1. Conclusion: EPSS can reliably diagnose reduced LVEF in a set of ED patients with cardiovascular symptoms. A cut off point at 9.5 mm has good sensitivity, specificity and Likelihood ratios.
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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 | 0.003 | 0.011 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".