P3562A new approach to diastolic blood pressure targets in patients with hypertension and coronary artery disease
Bibliographic record
Abstract
Background: Treatment of hypertension in the presence of coronary artery disease (CAD) can be challenging because myocardial perfusion is a function of the severity of coronary artery stenosis, level of diastolic blood pressure (DBP) which represents myocardial perfusion pressure and the amount of the myocardial mass being perfused. Excessive lowering of DBP may compromise coronary blood flow (CBF) but this is independent of the other determinants of CBF. Purpose: To examine a model which integrates these three CBF determinants in management of patients with hypertension Methods: We developed non-parametric equations that incorporate fractional flow reserve (FFR) measured at the time of coronary angiography and left ventricular mass measured by echocardiography and diastolic blood pressure (DBP). To validate and extrapolate the utility of this approach, a retrospective case review was conducted from the electronic medical records of persons attending a Cardiology Clinic. A consecutive patient sample (N=81) with CAD documented by coronary angiogram or coronary computerized tomography angiogram (CCTA) without previous PCI or valvular heart disease and with echocardiographic assessment of LV mass, was evaluated. FFR was estimated from the degree of coronary stenosis on coronary angiogram or CCTA. Blood pressure was measured in the office by an automated device.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".