Limb position affects magnitude of reactive hyperemia
Bibliographic record
Abstract
Several studies have reported a greater blood flow response to contractions when the limb is in the dependent compared to the independent position. These results have been interpreted as evidence for a skeletal muscle pump contribution to exercise hyperemia. An alternative explanation is that there are positional differences in myogenic tone and that a given stimulus has a greater effect when the myogenic tone is enhanced. We hypothesized that the magnitude of reactive hyperemia would be greater with the limb in the dependent position. Ten healthy volunteers of both sexes participated in the study. Brachial blood flow was measured by Doppler ultrasound and blood pressure measured by Finapres. The subjects lay supine with one arm supported in two different positions – above and below the heart. Reactive hyperemia was produced by occlusion of the arterial inflow with a blood pressure cuff inflated to 200mmHg for varying durations − 0.5 min, 1 min, 2 min, 5 min in randomized order. The peak increases in blood flow were 77±11,178±24, 291±25, 398±33 ml/min above the heart and 96±19, 279±62, 550±60, and 711±69 ml/min below the heart (p<0.01). These results show that a standard stimulus (reactive hyperemia) elicits different responses depending on limb position. Furthermore, the results suggest the need to reevaluate studies employing positional changes to examine muscle pump influences on exercise hyperemia.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".