Disease Activity in Mitral Annular Calcification
Bibliographic record
Abstract
Background: Mitral annular calcification (MAC) is associated with cardiovascular events and mitral valve dysfunction. However, the underlying pathophysiology remains incompletely understood. In this prospective longitudinal study, we used a multimodality approach including positron emission tomography, computed tomography, and echocardiography to investigate the pathophysiology of MAC and assess factors associated with disease activity and progression. Methods: A total of 104 patients (age 72±8 years, 30% women) with calcific aortic valve disease, therefore predisposed to MAC, underwent 18 F-sodium fluoride (calcification activity) and 18 F-Fluorodeoxyglucose (inflammation activity) positron emission tomography, computed tomography calcium scoring, and echocardiography. Sixty patients underwent repeat computed tomography and echocardiography after 2 years. Results: MAC (mitral annular calcium score >0) was present in 35 (33.7%) patients who had increased 18 F-fluoride (tissue-to-background ratio, 2.32 [95% CI, 1.81–3.27] versus 1.30 [1.22–1.49]; P <0.001) and 18 F-Fluorodeoxyglucose activity (tissue-to-background ratio, 1.44 [1.37–1.58] versus 1.17 [1.12–1.24]; P <0.001) compared with patients without MAC. MAC activity ( 18 F-fluoride uptake) was closely associated with the local calcium score and 18 F-Fluorodeoxyglucose uptake, as well as female sex and renal function. Similarly, MAC progression was closely associated with local factors, in particular, baseline MAC. Traditional cardiovascular risk factors and calcification activity in bone or remote atherosclerotic areas were not associated with disease activity nor progression. Conclusions: MAC is characterized by increased local calcification activity and inflammation. Baseline MAC burden was associated with disease activity and the rate of subsequent progression. This suggests a self-perpetuating cycle of calcification and inflammation that may be the target of future therapeutic interventions.
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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 | 0.003 |
| 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 | 0.000 |
| 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".