Echocardiography as a Screening Test for Myocardial Scarring in Children with Hypertrophic Cardiomyopathy
Bibliographic record
Abstract
Introduction. Hypertrophic cardiomyopathy (HCM) is burdened with morbidity and mortality including tachyarrhythmias and sudden cardiac death. These complications are attributed in part to the formation of proarrhythmic scars in the myocardium. The presence of extensive LGE is a risk factor for adverse outcomes in HCM. Late gadolinium enhancement (LGE) cardiac magnetic resonance imaging (cMRI) is the standard for the noninvasive evaluation of myocardial scars. However, echocardiography represents an attractive screening tool for myocardial scarring. The aim of this study was to compare the suitability of echocardiography to detect myocardial scars to the standard of cMRI-LGE.Methods. The cMRI studies and echocardiograms from 56 consecutive children with HCM were independently evaluated for the presence of cMRI-LGE and echocardiographic evidence of scarring by expert readers.Results. Echocardiography had a high sensitivity (93%) and negative predictive value (94%) in comparison to LGE. The false positive rate was high, leading to a low specificity (37%) and a low positive predictive value (35%).Conclusions. Given the poor specificity and positive predictive value, echocardiography is not a suitable screening test for the presence of myocardial scarring in children with HCM. However, children without echocardiographic evidence of myocardial scarring may not need to undergo cardiac magnetic resonance imaging to “rule in” LGE.
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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 | 0.002 | 0.007 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".