Insidious incrementalism: The silent failure of the microcirculation with increasing peripheral vascular disease risk
Bibliographic record
Abstract
This review summarizes material presented in "Adaptive Outcomes of Microvascular Networks to Obesity and Type II Diabetes/Insulin Resistance" on July 30, 2016, at the Joint Meeting of the American Physiological Society and the Physiological Society, in Dublin, Ireland. We discuss the poor predictive power of traditional markers of vascular dysfunction for functional outcomes of muscle fatigue-resistance and active hyperemia within the setting of elevated peripheral vascular disease risk. Using the obese Zucker rat model of the metabolic syndrome, we describe how blood flow distribution at arteriolar bifurcations (γ) is altered with PVD risk reflecting increased spatial heterogeneity of distribution within networks. The ability of the microvasculature to compensate for increased heterogeneity is attenuated in OZR, creating a condition wherein the inability to match perfusion to local demand is entrenched and made more difficult to overcome. This appears to be an incremental process, as multiple models of increased PVD risk manifest incremental shifts to the spatial and temporal behavior of γ. These data suggest that γ, a superior predictor of functional outcomes for skeletal muscle, may represent a broadly applicable concept that can inform us about system behavior, with health and increased disease/disease risk, and with imposition of therapeutic regimens.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".