Safety and performance benefits of arginine supplements for military personnel: a systematic review
Bibliographic record
Abstract
CONTEXT: Dietary supplements are widely used by military personnel and civilians for promotion of health. OBJECTIVE: The objective of this evidence-based review was to examine whether supplementation with l-arginine, in combination with caffeine and/or creatine, is safe and whether it enhances athletic performance or improves recovery from exhaustion for military personnel. DATA SOURCES: Information from clinical trials and adverse event reports were collected from 17 databases and 5 adverse event report portals. STUDY SELECTION: Studies and reports were included if they evaluated the safety and the putative outcomes of enhanced performance or improved recovery from exhaustion associated with the intake of arginine alone or in combination with caffeine and/or creatine in healthy adults aged 19 to 50 years. DATA EXTRACTION: Information related to population, intervention, comparator, and outcomes was abstracted. Of the 2687 articles screened, 62 articles meeting the inclusion criteria were analyzed. Strength of evidence was assessed in terms of risk of bias, consistency, directness, and precision. RESULTS: Most studies had few participants and suggested risk of bias that could negatively affect the results. l-Arginine supplementation provided little enhancement of athletic performance or improvements in recovery. Short-term supplementation with arginine may result in adverse gastrointestinal and cardiovascular effects. No information about the effects of arginine on the performance of military personnel was available. CONCLUSIONS: The available information does not support the use of l-arginine, either alone or in combination with caffeine, creatine, or both, to enhance athletic performance or improve recovery from exhaustion. Given the information gaps, an evidence-based review to assess the safety or effectiveness of multi-ingredient dietary supplements was not feasible, and therefore the development of a computational model-based approach to predict the safety of multi-ingredient dietary supplements is recommended.
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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 | 0.007 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".