A model based on clinical parameters to identify myocardial late gadolinium enhancement by magnetic resonance in patients with aortic stenosis: An observational study
Bibliographic record
Abstract
OBJECTIVE: With increasing age, the prevalence of aortic stenosis grows exponentially, increasing left heart pressures and potentially leading to myocardial hypertrophy, myocardial fibrosis and adverse outcomes. To identify patients who are at greatest risk, an outpatient model for risk stratification would be of value to better direct patient imaging, frequency of monitoring and expeditious management of aortic stenosis with possible earlier surgical intervention. In this study, a relatively simple model is proposed to identify myocardial fibrosis in patients with a diagnosis of moderate or severe aortic stenosis. DESIGN: Patients with moderate to severe aortic stenosis were enrolled into the study; patient characteristics, blood work, medications as well as transthoracic echocardiography and cardiovascular magnetic resonance were used to determine potential identifiers of myocardial fibrosis. SETTING: The Royal Brompton Hospital, London, UK. PARTICIPANTS: One hundred and thirteen patients in derivation cohort and 26 patients in validation cohort. MAIN OUTCOME MEASURES: Identification of myocardial fibrosis. RESULTS: Three blood biomarkers (serum platelets, serum urea, N-terminal pro-B-type natriuretic peptide) and left ventricular ejection fraction were shown to be capable of identifying myocardial fibrosis. The model was validated in a separate cohort of 26 patients. CONCLUSIONS: Although further external validation of the model is necessary prior to its use in clinical practice, the proposed clinical model may direct patient care with respect to earlier magnetic resonance imagining, frequency of monitoring and may help in risk stratification for surgical intervention for myocardial fibrosis in patients with aortic stenosis.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".