<scp>l</scp>-Arginine supplementation does not improve muscle function during recovery from resistance exercise
Bibliographic record
Abstract
The purpose of this study was to investigate the effects of l-arginine supplementation on muscle recovery after a single session of high-intensity resistance exercise (RE). Twenty healthy young adult participants (22.8 ± 3.4 years old) were assigned to 1 of 2 groups (N = 10 per group): a placebo-supplement group or an l-arginine-supplement group. The groups completed a session of high-intensity RE (0 h) and 3 subsequent fatigue test sessions (at 24, 48, and 72 h postexercise) to assess the time course of muscle recovery. During the test sessions, we assessed the following dependent variables: number of maximum repetitions, electromyographic signal (i.e., root mean square (RMS) and median frequency (MF) slope), muscle soreness, perceived exertion, blood levels of creatine kinase (CK) and lactate, and testosterone:cortisol ratio. Number of maximum repetitions increased at 48 and 72 h postexercise in both groups (time, P < 0.05). CK levels and muscle soreness increased at 24 h postexercise and then progressively returned to baseline at 72 h post exercise in both groups (time, P < 0.05). Lactate levels increased immediately postexercise but were reduced at 24 h postexercise in both groups (time, P < 0.05). Testosterone:cortisol ratio, RMS, and MF slope remained unchanged during the recovery period in both groups (time, P > 0.05). No significant (P > 0.05) group × time interaction was found for all dependent variables during the recovery period. In conclusion, our data indicate that l-arginine supplementation does not improve muscle recovery following a high-intensity RE session in young adults.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".