Severe Mitral Regurgitation in Paradoxical Low-Flow Low-Gradient Severe Aortic Stenosis
Bibliographic record
Abstract
Background: Patients with paradoxical low-flow, low-gradient severe aortic stenosis (LFLGAS) exhibit low transvalvular flow rate (Q), while maintaining preserved left ventricular ejection fraction (LVEF). Concomitant severe mitral regurgitation (MR) contributes to the low flow state, adding complexity to diagnosis and management. This study aimed to examine the impact of severe MR on outcomes in paradoxical LFLGAS. Methods: ), low transaortic gradients (mean gradient<40 mmHg), preserved LVEF (≥50%), and low flow rate (Q≤210 ml/sec), to confirm paradoxical LFLGAS. Subgroups were based on MR severity (severe and non-severe). Clinical outcomes included all-cause mortality, aortic valve replacement (AVR), heart failure hospitalizations, and a composite outcome. Results: In the severe MR group (n=80), patients had lower flow rates, increased LV dimensions and a more eccentric hypertrophy pattern compared to non-severe MR (n=1,109). Over a median 5-year follow-up, severe MR correlated with higher all-cause mortality (p=0.02) and AVR rates (p=0.012). After adjustment, severe MR was independently associated with increased all-cause mortality risk (HR=1.43, p=0.011) and composite outcome (HR=1.64, p<0.001). AVR significantly reduced mortality at every MR degree, with the most substantial impact in severe MR (HR=0.18, p<0.001). Propensity-adjusted models demonstrated a stronger AVR impact with increasing MR degree (p-for-interaction=0.044). Conclusions: Severe MR in paradoxical LFLGAS is associated with adverse outcomes and distinctive LV remodeling. Aortic valve replacement improves survival across all MR grades, with greater impact in severe MR.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".