Multimodality Quantitative Assessment of Aortic Regurgitation: A Systematic Review
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.010 |
| Bibliometrics | 0.019 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".