Blood flow in humans following low-load exercise with and without blood flow restriction
Bibliographic record
Abstract
Blood flow restriction (BFR) in combination with exercise has been used to increase muscle size and strength using relatively low loads (20%-30% 1-repetition maximum (1RM)). In research, the range of applied pressures based on a percentage of arterial occlusion pressure (AOP), is wide. The purpose of the study is to measure the blood flow response before exercise, following each set of exercise, and postexercise to low-load elbow flexion combined with no restriction (NOBFR), 40% of AOP (40BFR), and 80% of AOP (80BFR). One hundred and fifty-two participants volunteered; 140 completed the protocol (women = 75, men = 65). Participants were counter-balanced into 1 of 3 conditions. Following AOP and 1RM measurement, ultrasound was used to measure standing blood flow at rest in the right brachial artery. Participants performed 4 sets of elbow flexion at 30% 1RM. Blood flow was measured between sets and at 1 and 5 min postexercise. Blood flow decreased following inflation, with no difference between conditions (p < 0.001). Men had greater blood flow than women in all conditions at all time points (p < 0.001). Resting hyperemia decreased with pressure (NOBFR > 40BFR > 80BFR, p < 0.001). Blood flow increased from rest to after set 1 regardless of condition. Following cuff deflation, blood flow increased in both the 80BFR and 40BFR conditions. The reduction in hyperemia during BFR is pressure-dependent. Contrary to previous investigations, blood flow was increased above baseline following exercise.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".