Characteristics and outcome following transcatheter aortic valve replacement in patients with severe aortic stenosis with low flow
Bibliographic record
Abstract
AIMS: Only a few studies have examined the respective impact of low flow (LF), low gradient (LG) and low ejection fraction (LEF) on outcomes following transcatheter aortic valve replacement (TAVR). The purpose of this study was to assess the impact of preprocedural stroke volume index, aortic valve gradient, left ventricular ejection fraction (LVEF) and different flow/gradient/LVEF patterns on the clinical outcomes of patients with severe aortic stenosis (AS) who undergo TAVR. METHODS AND RESULTS: We analysed the clinical, echocardiographic, and outcome data collected in 770 patients with AS who underwent TAVR. Overall, 357 patients had normal flow (NF) AS and 413 had LF AS. Patients with NF had similar one-year mortality (12.0% vs. 15.0%, p=0.23) compared with those in the LF group. Overall, patients with NF and/or HG had lower one-year mortality rates (11.7 to 13%) compared to those with paradoxical LF-LG with NEF (19%) and those with classical LF-LG with LEF (27.3%). Low mean gradient was an independent predictor of all-cause mortality (hazard ratio: 1.14, per 10 mmHg decrease, p=0.02). Despite significant association in univariable analyses, LF and LEF were not found to be predictors of outcomes in multivariable analyses. CONCLUSIONS: Patients with HG and those with NF-LG have low one-year mortality rates following TAVR, whereas those with classical LF-LG and LEF and those with paradoxical LF-LG and NEF have high and intermediate risk of mortality, respectively. In contradiction to previous reports, LG but not LF or LEF is an independent predictor of late mortality in high-risk patients with severe AS undergoing TAVR.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".