Independent contribution of left ventricular ejection time to the mean gradient in aortic stenosis.
Bibliographic record
Abstract
BACKGROUND AND AIMS OF THE STUDY: Transvalvular mean pressure gradients (MPG) are important in the evaluation of aortic stenosis, but surprisingly they often differ in patients having similar valve effective orifice area (EOA) and stroke volume (SV). The study aim was to determine if these differences could be explained by variations in left ventricular ejection time (LVET). METHODS: A pulse duplicator system with a constant SV of 75 ml and incremental increases of LVET from 250 to 450 ms was used to measure MPG by Doppler echocardiography in three fixed stenoses (0.5, 1.0 and 1.5 cm2). The same variables were also measured at rest in 192 patients with isolated aortic stenosis (EOA <1.5 cm2) as well as during stress in a subgroup of 24 patients. RESULTS: In vitro, the increase in LVET produced marked decreases of MPG ranging from -40 mmHg (-45%) for the 0.5-cm2 stenosis to -22 mmHg (-61%) for the 1.5-cm2 stenosis. In vivo, MPG measured by Doppler correlated strongly (R2 = 0.83) with the MPG predicted by the formula: MPGpred [SV/(50xEOAxLVET)]2, and on this basis the relative contributions of EOA, SV and LVET to the variance of MPG were found to be 36, 34 and 13%, respectively. During stress, the contribution of LVET to the increase in MPG was variable, but was sometimes as important as that of SV. CONCLUSION: LVET may significantly and independently influence MPG in aortic stenosis. Clinically, variations of up to 15 mmHg in MPG may be observed uniquely on the basis of a change in duration of LVET, and hence the MPG cannot be used as a stand-alone parameter for serial evaluations or for comparisons of aortic stenosis severity between patients. A correction of MPG for LVET (in ms) such as MPGc = MPGx(LVET/300)2 might be helpful for rendering comparisons of MPG more meaningful in patients with aortic stenosis.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".