Multimodality Quantitative Assessment of Aortic Regurgitation: A Systematic Review
Why this work is in the frame
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Bibliographic record
Abstract
We performed a systematic review on the agreement and reproducibility of 3 advanced imaging methods, 3-dimensional echocardiography (3DE), cardiac computed tomography (CCT), and cardiac magnetic resonance (CMR), for quantifying aortic regurgitation (AR) severity. Medline, Embase, and Cochrane databases were systematically searched using the PICO model from inception to February 4, 2022, for publications that quantified AR severity with 3DE, CCT, or CMR. Measurement agreement and intraobserver and interobserver reproducibility results were extracted from each study. Study quality was assessed using the QUADAS-2 tool. Forty-two publications with 2176 patients with AR were identified. For 3DE, vena contracta (VC) width, VC area, and effective regurgitant orifice area had higher correlations with AR volume than the 2-dimensional echocardiography (2DE)-derived VC width. CCT-derived regurgitant volume had moderate-to-good correlations with 2DE. CMR regurgitant volume measurements had lower intraobserver and interobserver variabilities because of improved endocardial definition, fewer geometric assumptions, and less angle dependence for flow measurements when compared with 2DE. 3DE color flow convergence methods used to quantify AR severity were superior to 2DE methods and could be used in patients with adequate echocardiographic windows. CCT methods also demonstrated improvements over 2DE methods. Although this method is limited due to the radiation exposure, it could play a role in patients with poor echocardiographic windows unable to tolerate CMR. CMR demonstrated the smallest intraobserver and interobserver variability in evaluating AR severity and is a reasonable option for those where the echocardiographic results are mixed and for clinical trials.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.016 | 0.008 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it