Diagnostic yield of magnetic resonance imaging in heart failure with left ventricular dysfunction?
Bibliographic record
Abstract
Background: Heart failure with reduced ejection fraction is responsible for half of heart failure cases worldwide and implicates in substantial morbidity and mortality. However, even with clinical history and physical examination associated with conventional complementary exams, many patients remain without etiological diagnosis. Cardiac magnetic resonance has offered the possibility to clarify a variable proportion of these cases. Objective: To verify how much cardiac magnetic resonance contributes to etiologic diagnosis of heart failure with left ventricular ejection fraction <50% in a specialized service. Methods: We included individuals referred to cardiac magnetic resonance with heart failure and left ventricular ejection fraction <50% by transthoracic echocardiogram, without defined etiology, from January, 2017 to June, 2018 in a tertiary hospital. Results: The sample consisted of 87 patients, with average age of 45±16 years, 49% male and left ventricular ejection fraction 32%±13. Of the patients, 55,3% had etiological diagnosis through cardiac magnetic resonance: 33,4% myocarditis, 11.5% non-compaction cardiomyopathy, 6.8% Chagas disease, and for hypertensive heart disease, amyloidosis and arrhythmogenic right ventricle dysplasia, 1,2% each. Late gadolinium enhancement was positive in 61% and non-ischemic pattern predominated (50,5%). Reverse remodeling occurred with normalization of ventricular function in 13% of patients. Conclusion: The performance of cardiac magnetic resonance in patients without etiologic diagnosis of HF with left ventricle dysfunction is clinically significant, since it contributed more than 50% of the time to the etiology and prognosis of patients. This positive impact occurred in a tertiary cardiology teaching service, so it is possible that in other circumstances the role of the cardiac magnetic resonance may be even greater than that here presented.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".