Alterações da Rigidez Arterial em Pacientes com Estenose Aórtica Grave Submetidos à Cirurgia de Troca Valvar
Bibliographic record
Abstract
BACKGROUND: Little is known about the impact of severe aortic stenosis (AS) in aortic stiffness and if there is any change after removing AS barrier with aortic valve replacement (AVR) surgery. OBJECTIVE: To estimate carotid-femoral pulse wave velocity (PWV) changes after AVR surgery and to define PWV predictors in severe AS patients. METHODS: Single-center retrospective cohort, including patients with severe AS who underwent AVR surgery with bioprostheses, between February 2017 and January 2019 and performed PWV measurements (Complior®) before and after the procedure (2±1 months). Before and after AVR, PWV values were compared through paired tests. The associations of PWV with clinical data were studied and linear regression models were applied to estimate pre and postoperative PWV independent predictors. The significance level was set at 5%. RESULTS: We included 150 patients in the sample, with mean age of 72±8 years, and 51% being males. We found a statistically significant increase in PWV values after surgery (9.0±2.1 m/s vs. 9.9±2.2, p<0.001, before and after AVR, respectively) and an inverse association with AS severity variables. In the linear regression model, age and systolic blood pressure (SBP) were established as independent predictors of higher pre- and postoperative PWV, while higher mean valvular gradient emerged as a determinant of lower pre-AVR PWV. CONCLUSION: We documented an inverse correlation of arterial stiffness with the severity of AS in patients with AS, and a significant increase in PWV values after AVR surgery. Advanced age and higher SBP were associated with higher PWV values, although arterial function measurements were within the normal range. (Arq Bras Cardiol. 2021; 116(3):475-482).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".