What do you mean by aortic valve area: geometric orifice area, effective orifice area, or gorlin area?
Bibliographic record
Abstract
Aortic valve area can be measured by cardiac catheterization, Doppler echocardiography, or imaging planimetry to assess aortic stenosis severity. These diagnostic techniques provide the Gorlin area, the effective orifice area (EOA) and the geometric orifice area (GOA), respectively. The differences between these three parameters depend mainly on the valve inflow shape and cross-sectional area of the ascending aorta. Because the values obtained may differ noticeably in the same patient, they may lead to different estimations of stenosis severity depending on the measurement method used. It is therefore essential to be aware of the underlying fundamentals on which these parameters are based. The aim of this state-of-the-art report was to clarify these hemodynamic concepts and to underline their clinical implications. Because planimetry only provides GOA and does not characterize the flow property, this method should preferably not be used to assess stenosis severity. The most appropriate parameters for this purpose are the Gorlin area and the energy loss coefficient (E(L)Co), which corresponds to the EOA adjusted for aortic cross-sectional area. From a hemodynamic viewpoint, Doppler E(L)Co and Gorlin area both reflect the fluid energy loss induced by aortic stenosis, and describe better the increased overload imposed on the left ventricle. Although the Gorlin area and Doppler E(L)Co are equivalent, the latter parameter has the advantage of being measurable non-invasively using Doppler echocardiography.
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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.006 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".