Heart failure with normal left ventricular ejection fraction
Bibliographic record
Abstract
PURPOSE OF REVIEW: Nearly half of patients presenting with heart failure have a preserved left ventricular ejection fraction (LVEF), previously known as diastolic heart failure. The diagnosis requires fulfillment of three criteria: signs or symptoms of heart failure, presence of a normal LVEF, and evidence of diastolic dysfunction. Two of the criteria can be evaluated by echocardiography. This article reviews the echocardiographic approach to the patient with suspected heart failure with a normal left ventricular ejection fraction (HFNEF). RECENT FINDINGS: Echocardiography is the primary modality for evaluating left ventricular (LV) systolic and diastolic function in heart failure patients. Measurements of LVEF from two-dimensional echocardiography can have significant variability despite the use of quantitative methods. The use of contrast agents and three-dimensional echocardiography can improve the accuracy. Newer modalities of tissue Doppler imaging and deformation imaging are challenging the concept that systolic function is preserved in HFNEF. Evaluation of diastolic function with echocardiography requires a comprehensive approach using multiple modalities to quantitate transmitral flow, pulmonary venous flow, mitral annular motion, myocardial deformation, and cardiac structure. The clinical applicability of parameters used for evaluating diastolic function and filling pressures is dependent on the LVEF, necessitating a unique approach in patients with suspected HFNEF. SUMMARY: A comprehensive examination with knowledge of the potential limitations of echocardiography is required to accurately interpret LV systolic and diastolic function in patients with suspected HFNEF.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".