Muscle-specific neuromuscular responses during running with blood flow restriction
Bibliographic record
Abstract
Abstract The purpose of this investigation was to examine muscle excitation at maximal running capacity without blood flow restriction (BFR) relative to submaximal running bouts with BFR. Fourteen college-aged males randomly completed four, three-minute running bouts at 70, 80, and 90% of peak speed with BFR (70%BFR, 80%BFR, and 90%BFR) and without BFR at 100% of their peak speed (100%NOBFR). The surface electromyographic amplitudes of the vastus lateralis, rectus femoris, and vastus medialis muscles were assessed. Muscle excitation of the vastus lateralis was similar across most bouts; however, it was lower during the 70%BFR compared to 90%BFR (Meandiff =−4.67±− 0.22%; Bayesian 95% high-density interval [HDI]: − 7.07 to − 2.2) and 100%NOBFR (Meandiff =−2.94±− 0.27%; 95% HDI:−5.33 to−0.50) bouts. For the rectus femoris, muscle excitation largely increased across running speeds (70%BFR<80%BFR < 90%BFR; 90%BFR < 100%NOBFR). For the vastus medialis, muscle excitation was lower during the 70%BFR compared to 90%BFR (Meandiff =−8.15±− 0.28%; 95% HDI:−15.3 to−0.89). Submaximal running with BFR increased muscle excitation responses for the vastus lateralis and vastus medialis, such that muscle excitation was similar to 100%BFR, despite 10–20% reductions in running speed. The rectus femoris muscle, however, was not affected by BFR during the submaximal running bouts.
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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) | 0.001 | 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".