Video Gaming Reduces Circulating Creatine Levels in Young Male E-Gamers
Bibliographic record
Abstract
Background: No studies so far assessed whether acute video gaming affects creatine metabolism, a metabolic pathway critical in replenishing immediate energy for cells and tissues with high and intermittent energy fluctuations. In this study, we explored whether a single session of prolonged video gaming alters circulating biomarkers of creatine metabolism in young male e-gamers. Methods: A total of 12 young men (age 25.6 3.8 years) signed an informed consent to volunteer in this quasi-experimental before-after pilot trial. Each participant took part in a single 6-h session of competitive online ranked matches in a popular tactical first-person shooting game. Results: A 6-h video gaming session resulted in a statistically significant drop in serum creatine levels (from 27.6 7.5 µmol/L at baseline to 22.9 8.3 µmol/L at follow-up; P = 0.029). The mean reduction in serum creatine was 4.70 µmol/L (95% confidence interval (CI): - 2.3 to 11.7), with a moderate-to-large effect size (d = 0.59). Serum creatinine concentrations tended to drop after the gaming session from 88.1 15.5 to 78.2 19.8 µmol/L (P = 0.077). Conclusion: Our findings indicate that creatine homeostasis is sensitive to video gaming perhaps owing to more creatine from the circulation utilized as an energy source for active tissues, including the brain. J Endocrinol Metab. 2024;14(1):59-62 doi: https://doi.org/10.14740/jem923
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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) | 0.002 | 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".