Endothelial function assessment: flow-mediated dilation and constriction provide different and complementary information on the presence of coronary artery disease
Bibliographic record
Abstract
AIMS: A number of risk factors for atherosclerosis have been identified, but it remains difficult, on an individual patient basis, to predict how these factors interact in determining the development of coronary artery disease (CAD). It also remains unclear whether the study of endothelial function provides information that is additive to that of traditional risk factors. METHODS AND RESULTS: Flow-mediated dilation (FMD) and low-flow-mediated constriction (L-FMC) were measured in 451 consecutive patients before coronary angiography. Low-flow-mediated constriction (P< 0.0001) and FMD (P=0.0005) progressively decreased with the number of diseased vessels, and L-FMC showed a significant linear correlation with the SYNTAX score (R=0.38; P< 0.0001). Logistic regression analysis confirmed the association between endothelial function parameters and CAD (P=0.001 for L-FMC, P=0.02 for FMD). Receiver operating characteristic analysis demonstrated that the addition of L-FMC alone and of the combination of FMD and L-FMC improved the predictive power of a model based on traditional risk factors for CAD (area under the curve of the risk factor model=0.716; risk factor model + FMD=0.734, P=0.1 compared with risk factor model; risk factor model + L-FMC=0.771, P=0.004; risk factor model + L-FMC + FMD=0.779, P=0.002). Reclassification statistics showed that the introduction of FMD to the model based on the traditional risk factors correctly reclassified an additional 5% of patients, and that the introduction of L-FMC net correctly reclassified 19% of the patients. There was no correlation between different parameters of endothelial function. CONCLUSION: Endothelial function assessment provides modest but statistically significant additional information in predicting the presence of CAD.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".