Ejection Fraction Velocity Ratio as an Indicator of Aortic Stenosis Severity
Bibliographic record
Abstract
BACKGROUND: Despite the widespread use of the continuity equation in the estimation of aortic valve area (AVA) in patients with aortic stenosis, it is subject to errors, time consuming, and can be technically demanding. As such, simpler methods of assessing aortic stenosis severity have been pursued. METHODS: The ejection fraction velocity ratio [EFVR = ejection fraction (%) / maximal aortic velocity (m/sec)] was compared to AVA determined with the continuity equation in 857 patients with aortic stenosis and varying degrees of LV systolic dysfunction. Severe aortic stenosis was defined as an AVA < 1.0 cm2. RESULTS: There was good to excellent correlation between our index and aortic valve area (P < 0.001 for each ejection fraction subgroup). Receiver operating characteristic analysis showed that the EFVR functioned well with areas under the curve between 0.893 and 0.938. CONCLUSION: The EFVR is a simple noninvasive method for screening patients for an AVA of 1.0 cm2. It could be used as a screening test or in lieu of the continuity equation particularly when there is problematic measurement of either the LVOT diameter or velocity.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".