Impact of systemic hypertension on the assessment of aortic stenosis
Bibliographic record
Abstract
OBJECTIVE: To determine the effect of systemic arterial hypertension on the indices of aortic stenosis (AS) severity. METHODS: A severe supravalvar AS was created in 24 pigs. The maximum and mean pressure gradients across the stenosis were measured by Doppler echocardiography and by catheterisation. Both echocardiography and catheter data were used to calculate stenosis effective orifice area, energy loss coefficient, and peak systolic left ventricular wall stress. Measurements were taken both at normal aortic pressures and during hypertension induced by banding of the distal thoracic aorta in 14 pigs and by intravenous administration of phenylephrine in 10 pigs. RESULTS: During hypertension, systemic arterial resistance downstream from the stenosis increased greatly (all animals: 71 (40)%), whereas total systemic arterial compliance decreased significantly (-38 (21)%). Hypertension resulted in a moderate increase in effective orifice area (29 (14)%) and energy loss coefficient (25 (17)%) and substantial decreases in catheter gradients (maximum: -40 (20)%; mean: -43 (20)%; peak to peak: -70 (23)%) and Doppler gradients (maximum: -35 (17)%; mean: -37 (16)%). In multivariate analysis, peak to peak gradient was significantly (p < 0.001) related to the energy loss coefficient, mean flow rate, and arterial compliance, whereas maximum and mean catheter gradients were related only to the energy loss coefficient and flow rate. Of major importance, maximum systolic left ventricular wall stress increased greatly during hypertension (43 (23)%). CONCLUSIONS: The severity of AS may be partially masked by the presence of coexisting hypertension. The markers of AS severity should thus be interpreted with caution in hypertensive patients and be re-evaluated when the patient is in a normotensive state.
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 | 0.001 |
| 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".