“Heads Up” for Creatine Supplementation and its Potential Applications for Brain Health and Function
Bibliographic record
Abstract
There is emerging interest regarding the potential beneficial effects of creatine supplementation on indices of brain health and function. Creatine supplementation can increase brain creatine stores, which may help explain some of the positive effects on measures of cognition and memory, especially in aging adults or during times of metabolic stress (i.e., sleep deprivation). Furthermore, creatine has shown promise for improving health outcome measures associated with muscular dystrophy, traumatic brain injury (including concussions in children), depression, and anxiety. However, whether any sex- or age-related differences exist in regard to creatine and indices of brain health and function is relatively unknown. The purpose of this narrative review is to: (1) provide an up-to-date summary and discussion of the current body of research focusing on creatine and indices of brain health and function and (2) discuss possible sex- and age-related differences in response to creatine supplementation on brain bioenergetics, measures of brain health and function, and neurological diseases.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".