Quantitative aortography for assessment of aortic regurgitation in the era of percutaneous aortic valve replacement
Bibliographic record
Abstract
Paravalvular leak (PVL) is a shortcoming that can erode the clinical benefits of transcatheter valve replacement (TAVR) and therefore a readily applicable method (aortography) to quantitate PVL objectively and accurately in the interventional suite is appealing to all operators. The ratio between the areas of the time-density curves in the aorta and left ventricular outflow tract (LVOT-AR) defines the regurgitation fraction (RF). This technique has been validated in a mock circulation; a single injection in diastole was further tested in porcine and ovine models. In the clinical setting, LVOT-AR was compared with trans-thoracic and trans-oesophageal echocardiography and cardiac magnetic resonance imaging. LVOT-AR > 17% discriminates mild from moderate aortic regurgitation on echocardiography and confers a poor prognosis in multiple registries, and justifies balloon post-dilatation. The LVOT-AR differentiates the individual performances of many old and novel devices and is being used in ongoing randomized trials and registries.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".