Mixed aortic stenosis and regurgitation: a clinical conundrum
Bibliographic record
Abstract
Mixed aortic stenosis (AS) and aortic regurgitation (AR) is the most frequent concomitant valve disease worldwide and represents a heterogeneous population ranging from mild AS with severe AR to mild AR with severe AS. About 6.8% of patients with at least moderate AS will also have moderate or greater AR, and 17.9% of patients with at least moderate AR will suffer from moderate or greater AS. Interest in mixed AS/AR has increased, with studies demonstrating that patients with moderate mixed AS/AR have similar outcomes to those with isolated severe AS. The diagnosis and quantification of mixed AS/AR severity are predominantly echocardiography-based, but the combined lesions lead to significant limitations in the assessment. Aortic valve peak velocity is the best parameter to evaluate the combined haemodynamic impact of both lesions, with a peak velocity greater than 4.0 m/s suggesting severe mixed AS/AR. Moreover, symptoms, increased left ventricular wall thickness and filling pressures, and abnormal left ventricular global longitudinal strain likely identify high-risk patients who may benefit from closer follow-up. Although guidelines recommend interventions based on the predominant lesion, some patients could potentially benefit from earlier intervention. Once a patient is deemed to require intervention, for patients receiving transcatheter valves, the presence of mixed AS/AR could confer benefit to those at high risk of paravalvular leak. Overall, the current approach of managing patients based on the dominant lesion might be too reductionist and a more holistic approach including biomarkers and multimodality imaging cardiac remodelling and inflammation data might be more appropriate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".