Ventricular pressure-volume loop and other heart function metrics can elucidate etiology of failure of TAVI and interventions
Bibliographic record
Abstract
Aortic valve stenosis is one of the most acute and chronic cardiovascular disease conditions. Bicuspid aortic valve is the most common congenital heart abnormality and affected individuals have a 50% chance of developing severe aortic valve stenosis during their lifetime. In aortic valve disease (both aortic valve stenosis and bicuspid aortic valve), the heart valves are damaged and do not work properly. This condition can rapidly affect the pumping action of the heart and can progress to heart failure. Heart failure is a deadly disease affecting at least 26 million people worldwide and its prevalence is increasing with high mortality and morbidity.1 Most importantly, aortic valve disease commonly coexists with other cardiovascular diseases, giving rise to the most general yet fundamentally challenging scenario: complex valvular, ventricular, and vascular diseases (C3VD). In C3VD, multiple valvular, ventricular, and vascular pathologies interact with one another, while the physical phenomena associated with each pathology amplify the effects of others on the cardiovascular system. Left ventricle (LV) pressure-volume (P-V) loop analysis is a powerful tool to assess cardiac mechanics. This analysis can reveal the pathophysiological mechanisms of heart failure, including heart failure with preserved ejection fraction and myocardial and valvular diseases. It is, therefore, instrumental in...
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".