Estimation of the End of Ejection in Aortic Stenosis
Bibliographic record
Abstract
BACKGROUND: All indices of aortic stenosis (AS) rely on measurements of mean transvalvular pressure gradient (DeltaP) and flow rate. Because the gradient is reversed during late ejection, the late systolic left ventricular (LV)-aortic pressure crossover may be an erroneous landmark of end-ejection. The aortic incisura should be a better reference to calculate indices of AS invasively. METHODS AND RESULTS: The accuracy of the pressure crossover and the incisura to define end-ejection was assessed in a chronic AS experimental model (9 dogs) with the use of an implantable flowmeter and Doppler echocardiography as reference. In 288 hemodynamic recordings analyzed (aortic valve area [AVA]: 0.74+/-0.46 cm2), ejection ended 37+/-29 ms after the pressure crossover but almost simultaneously with the incisura (2+/-17 ms). Pressure crossover error accounted for significant errors in the measurement of DeltaP (95% limits of agreement, +0 to +7 mm Hg) and AVA (-0.1 to +0.2 cm2). These errors were reduced to less than half with the use of the incisura to define end-ejection. Additionally, the agreement with Doppler-derived AS indices was best with use of the incisura. Pressure crossover error was maximal in situations of higher output, moderate orifice narrowing, higher arterial compliance, and lower vascular resistance. In 32 consecutive patients undergoing cardiac catheterization for AS, the pressure crossover induced a clinically important overestimation of the DeltaP from +22 to +50%. Errors in AVA estimation were considerably smaller (-2% to +6%) because of simultaneous and offsetting errors in the measurements of DeltaP and flow. CONCLUSIONS: The aortic incisura and not the second pressure crossover should be used to obtain invasive indices of AS.
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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 | 0.000 |
| 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 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".