Human microvascular reactivity: a review of vasomodulating stimuli and non-invasive imaging assessment
Bibliographic record
Abstract
The microvasculature serves an imperative function in regulating perfusion and nutrient exchange throughout the body, adaptively altering blood flow to preserve hemodynamic and metabolic homeostasis. Its normal functioning is vital to tissue health, whereas its dysfunction is present in many chronic conditions, including diabetes, heart disease, and cognitive decline. As microvascular dysfunction often appears early in disease progression, its detection can offer early diagnostic information. To detect microvascular dysfunction, one uses imaging to probe the microvasculature's ability to react to a stimulus, also known as microvascular reactivity (MVR). An assessment of MVR requires an integrated understanding of vascular physiology, techniques for stimulating reactivity, and available imaging methods to capture the dynamic response. Practical considerations, including compatibility between the selected stimulus and imaging approach, likewise require attention. In this review, we provide a comprehensive foundation necessary for informed imaging of MVR, with a particular focus on the challenging endeavor of assessing microvascular function in deep tissues.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".