Fueling the Firefighter and Tactical Athlete with Creatine: A Narrative Review of a Key Nutrient for Public Safety
Bibliographic record
Abstract
Background/Objectives: Firefighters, tactical police officers, and warriors often engage in periodic, intermittent, high-intensity physical work in austere environmental conditions and have a heightened risk of premature mortality. In addition, tough decision-making challenges, routine sleep deprivation, and trauma exacerbate this risk. Therefore, identifying strategies to bolster these personnel’s health and occupational performance is critical. Creatine monohydrate (CrM) supplementation may offer several benefits to firefighters and tactical athletes (e.g., police, security, and soldiers) due to its efficacy regarding physical performance, muscle, cardiovascular health, mental health, and cognitive performance. Methods: We conducted a narrative review of the literature with a focus on the benefits and application of creatine monohydrate among firefighters. Results: Recent evidence demonstrates that CrM can improve anaerobic exercise capacity and muscular fitness performance outcomes and aid in thermoregulation, decision-making, sleep, recovery from traumatic brain injuries (TBIs), and mental health. Emerging evidence also suggests that CrM may confer an antioxidant/anti-inflammatory effect, which may be particularly important for firefighters and those performing tactical occupations exposed to oxidative and physiological stress, which can elicit systemic inflammation and increase the risk of chronic diseases. Conclusions: This narrative review highlights the potential applications of CrM for related tactical occupations, with a particular focus on firefighters, and calls for further research into these populations.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".