Mechanistic classification of isolated severe aortic regurgitation in a contemporary cohort of patients
Bibliographic record
Abstract
Abstract Aims Aortic regurgitation (AR) arises from leaflet disease and/or dilatation of the functional aortic annulus complex. Understanding the mechanisms of AR informs surgical planning of valve and aorta repair. This study investigates the mechanisms, aetiologies, and outcomes of isolated native severe AR in a consecutive cohort of patients. Methods and results Patients with moderate-to-severe (3+)/severe (4+) native valve AR, identified from our institutional echocardiography database (2014–2018), were included. Exclusions were significant concomitant valve disease, endocarditis, or aortic dissection. AR was classified per the El-Khoury system: Type I (normal leaflet motion: Ia–ascending aorta/sinotubular junction dilatation, Ib–aortic root dilation, Ic–aortic annular dilation), Type II (leaflet prolapse), and Type III (leaflet restriction). Valve anatomy and clinical outcomes, including mortality and surgical intervention, were analyzed. Of 282 patients (77.3% male), 58.5% had multiple AR mechanisms. Type II (leaflet prolapse) was most common (48.6%), followed by Type III (36.2%). Bicuspid aortic valve (BAV) represented 35.5% of the population, with leaflet prolapse observed in 72%. Multiple mechanisms were more frequent in BAV (77% vs. 48%, P < 0.001). After a median follow-up of 4.7 years (available for 97.5% of patients), 158 (57.5%) underwent an intervention with 48.7% having an aortic valve repair or valve-sparing aortic root replacement. Conclusion Although leaflet prolapse (Type II) was the pre-dominant AR mechanism, multiple contributing mechanisms were often present, particularly in BAV patients. Aortic valve repair accounted for nearly half of surgical interventions, underscoring the importance of mechanism identification to optimize repair and avoid valve replacement.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".