A little calcium goes a long way: rethinking early calcific aortic valve disease
Bibliographic record
Abstract
This editorial refers to ‘Impact of age on aortic valve calcium progression and risk for aortic stenosis: multi-ethnic study of atherosclerosis’, by N. Marrero et al., https://doi.org/10.1093/ehjci/jeaf279. Aortic stenosis (AS) is the most common valvular heart disease leading to intervention in high-income countries. Pathophysiologically, it is characterized by the progressive deposition of fibrosis and calcification on the aortic valve leaflets. The severity of AS is primarily assessed using echocardiography; however, the measurement of aortic valve calcification (AVC) by computed tomography (CT) has emerged as a valuable complementary tool for refining severity assessment.1 Importantly, CT imaging can detect early stages of AVC even when echocardiographic haemodynamic parameters remain within normal limits. Once these haemodynamic markers begin to change, even mildly, it is well established that patients will continue to progress to moderate and eventually severe AS, although the rate of progression varies between individuals. Despite this, only about 2% of patients with aortic sclerosis progress to clinically significant AS per year,2 highlighting the need for careful risk stratification and longitudinal monitoring.
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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 | 0.003 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.051 | 0.050 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".