Isolated Left Ventricular Noncompaction as a Cause for Heart Failure and Heart Transplantation: A Single Center Experience
Bibliographic record
Abstract
OBJECTIVES: To determine the prevalence of isolated left ventricular noncompaction (IVNC) as a cause of heart failure and heart transplantation. METHODS: There were 960 patients seen in the heart failure clinic from 1987 to 2005, with a complete evaluation including echocardiography at our center (study population, 82% men, mean age 52 years). The following data were collected: type of heart disease, age at echocardiography and at heart transplantation, and frequency of heart transplantation. Echocardiographic diagnosis of IVNC was based on our published criteria. RESULTS: The etiologies of heart failure were coronary artery disease (CAD; 37%), idiopathic dilated cardiomyopathy (33%), valvular heart disease (11%), congenital heart disease (5%), IVNC (3%), hypertensive heart disease (3%), hypertrophic cardiomyopathy (2%), myocarditis (1%), and <1% other diagnoses. Heart transplantation was performed in 253 patients (26%) due to idiopathic dilated cardiomyopathy (42%), CAD (39%), valvular heart disease (5%), congenital heart disease (5%), IVNC (2%), or other etiologies (< or =1% each). CONCLUSIONS: The most common causes for heart failure remain idiopathic dilated cardiomyopathy, CAD and valvular heart disease. Strictly using the criteria for the definition of IVNC, IVNC is a rare underlying cardiomyopathy for both, heart failure (2.7%) and heart transplantation (2%) in our center.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".