Bibliographic record
Abstract
During muscle contraction, several mechanisms regulate blood flow to ensure a close coupling between muscle oxygen delivery and metabolic demand. No single factor has been identified to constitute the primary metabolic regulator, yet there are signal transduction pathways between skeletal muscle and the vasculature that induce vasodilation. A link between muscle metabolic events and microvascular control of blood flow is illustrated by local dilation of terminal arterioles during contraction of muscle fibers and conduction of vasodilation upstream. Endothelial-derived vasodilator mechanisms are known to exert control of muscle vasodilation. Adenosine, nitric oxide (NO), prostacyclin (PGI2), and endothelial-derived hyperpolarization factor (EDHF) are possible mediators of muscle vasodilation during exercise. In humans, adenosine has been shown to contribute to functional hyperemia as blood flow is reduced under nonselective adenosine-receptor blockade. No clear role has been demonstrated for either NO or PGI2(2), based on studies employing selective inhibition of these substances individually, suggesting a redundancy of vasodilator mechanisms. This is supported by recent work demonstrating that combined blockade of NOS and PGI2, and NOS and cytochrome P450, both attenuate exercise-induced hyperemia in humans. Combined vasodilator blockade studies offer the potential to uncover important interactions and compensatory vasodilator responses. The signaling pathways that link metabolic events evoked by muscle contraction to vasodilatory signals in the local vascular bed remains an important area of study.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".