Defining abnormal flow-mediated slowing of brachial–radial pulse wave velocity, a noninvasive vasoreactivity test
Bibliographic record
Abstract
OBJECTIVES: Flow-mediated slowing (FMS) reflects macrovascular reactivity by quantifying the decline in brachial-radial pulse wave velocity (PWV) during reactive hyperaemia. We identified abnormal FMS response using normal values and integrative algorithms. METHODS: In this cross-sectional, observational study, 408 community-dwelling individuals underwent FMS testing with 5 min of upper arm occlusion. FMS was assessed at 30 s intervals for 4 min postocclusion. From 76 healthy individuals, we extracted limits of normality for peak FMS, defining an abnormal peak response if PWV slowed by less than 9.4% (if <60 years) or 4.6% (if ≥60 years). Group-based trajectory modelling (GBTM) assigned participants to distinct FMS response groups. Multivariable regression identified clinical correlates of the FMS response groups. RESULTS: Higher age correlated independently with less decline in PWV in the early phase ( P ≤ 0.0076 for 0-30 s), whereas higher SBP and no beta blocker use were linked to less decline overall (SBP: P ≤ 0.048 for 0-210 s; beta blockers: P ≤ 0.014 for 0-180 s). Abnormal peak FMS was associated with higher SBP [adjusted odds ratio (OR): 1.31, P = 0.0017) and less use of beta blockers (adjusted OR: 0.44, P = 0.041). A three-group GBTM model identified a low, moderate and high FMS response group. The risk for a low FMS response increased with age, SBP and no use of beta blockers ( P ≤ 0.038 for all). CONCLUSION: Abnormal FMS response was linked to cardiovascular risk factors such as ageing, hypertension and beta blocker use. The FMS response patterns may enable qualitative interpretation of FMS tests, though validation against hard clinical outcomes is warranted.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".