Scleraxis as a prognostic marker of myocardial fibrosis in hypertrophic cardiomyopathy (SPARC) study
Bibliographic record
Abstract
Interstitial fibrosis is a histopathological hallmark of hypertrophic cardiomyopathy (HCM). Although extracellular matrix (ECM) biomarkers, including matrix metalloproteinases, are overexpressed in HCM patients, they do not correlate with sudden cardiac death (SCD) risk. The objective of this study was to determine whether scleraxis, a transcription factor that regulates collagen gene expression, is detectable in HCM patients and correlates with disease burden. Between 2017 and 2018, a total of 46 HCM patients were enrolled (58 ± 14 years (31 males, 15 females)) with a mean 5 year SCD risk of 2.3% ± 1.3%. Cardiac MRI confirmed HCM in all patients with a mean interventricular septal thickness of 20 ± 2 mm. Late gadolinium enhancement (LGE) was present in 32 (70%) study participants occupying 18% ± 7% of the left ventricular (LV) myocardium. Serum scleraxis levels were significantly higher in the HCM patients by approximately twofold as compared to controls (0.76 ± 0.06 versus 0.32 ± 0.02 ng/mL, p < 0.05). No correlation was demonstrated between serum scleraxis levels and markers of disease severity in HCM patients, including maximum LV wall thickness, %LGE, and SCD risk factors. Serum scleraxis is elevated in the HCM population. Future studies are warranted to evaluate the prognostic value of scleraxis in identifying high-risk HCM patients who require aggressive management for prevention of SCD.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".