Assessment of vascular function: flow-mediated constriction complements the information of flow-mediated dilatation
Bibliographic record
Abstract
OBJECTIVE: To determine whether vascular function assessed by low-flow-mediated constriction (L-FMC), a novel non-invasive method, complements the information obtained with "traditional" flow-mediated dilatation (FMD). DESIGN AND PATIENTS: In protocol 1, 12 healthy young volunteers underwent FMD and L-FMC measurements at rest and immediately after isometric exercise of the same hand. In protocol 2, 24 patients with coronary artery disease, 24 with congestive heart failure, 24 hypertensive patients and 64 healthy volunteers were enrolled to undergo L-FMC and FMD measurements. RESULTS: In protocol 1, exercise was associated with mean (SD) increases in radial artery blood flow, diameter and L-FMC (from -5.1 (1.5)% to -7.8 (3.4)%, p<0.05), while FMD was significantly blunted (from 6.0 (2.4)% to 3.0 (3.2)%, p<0.05). In protocol 2, both FMD and L-FMC were blunted in the patient groups. Receiver operating curve analysis showed that, as compared with FMD alone, the combination of L-FMC and FMD significantly improved the sensitivity and specificity in detecting patients diagnosed with cardiovascular disease (p<0.05). CONCLUSION: In the first protocol, FMD and L-FMC were shown to be reciprocally regulated. A blunted FMD may, in certain cases, be an expression of increased resting vascular activation and not only of impaired endothelial function. In the second protocol, a statistical approach showed that implementation of L-FMC provides a better characterisation than FMD of vascular function in cardiovascular disease. Vascular (endothelial) function is a complex phenomenon which requires a multifaceted approach; it is suggested that a combination of L-FMC and FMD will provide additive and complementary information to "traditional" FMD measurements.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".