Competition for blood flow distribution between respiratory and locomotor muscles: implications for muscle fatigue
Bibliographic record
Abstract
Sympathetically induced vasoconstrictor modulation of local vasodilation occurs in contracting skeletal muscle during exercise to ensure appropriate perfusion of a large active muscle mass and to maintain also arterial blood pressure. In this synthesis, we discuss the contribution of group III-IV muscle afferents to the sympathetic modulation of blood flow distribution to locomotor and respiratory muscles during exercise. This is followed by an examination of the conditions under which diaphragm and locomotor muscle fatigue occur. Emphasis is given to those studies in humans and animal models that experimentally changed respiratory muscle work to evaluate blood flow redistribution and its effects on locomotor muscle fatigue, and conversely, those that evaluated the influence of coincident limb muscle contraction on respiratory muscle blood flow and fatigue. We propose the concept of a “two-way street of sympathetic vasoconstrictor activity” emanating from both limb and respiratory muscle metaboreceptors during exercise, which constrains blood flow and O 2 transport thereby promoting fatigue of both sets of muscles. We end with considerations of a hierarchy of blood flow distribution during exercise between respiratory versus locomotor musculatures and the clinical implications of muscle afferent feedback influences on muscle perfusion, fatigue, and exercise tolerance.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".