Caffeine Boosts Weight-Lifting Performance in Rats: A Pilot Study
Bibliographic record
Abstract
Caffeine is a well-described ergogenic aid used to enhance athletic performance. Using animal models can greatly increase our understanding of caffeine’s mechanisms in performance. Here, we adapted an animal weight-lifting exercise model to demonstrate caffeine’s ergogenic effect in rats. Male Wistar rats (315 ± 35 g) were randomly divided into two groups: one group received 5 mg·kg−1 of caffeine (0.5 mL; CEx; n = 5) and the other 0.9% NaCl (0.5 mL; PEx; n = 4) through an orogastric probe (gavage) one hour before exercise. Weight-lifting exercise sessions were performed over three subsequent days, and the number of complete squats performed was counted. Analyses of the area under the curve in all three experiments showed that the CEx group responded more to stimuli, performing more squats (1.7-, 2.0-, and 1.6-fold; p < 0.05) than the control group did. These three days’ data were analyzed to better understand the cumulative effect of this exercise, and a hyperbolic curve was fitted to these data. Data fitting from the caffeine-supplemented group, CEx, also showed larger Smax and Kd (2.3-fold and 1.6-fold, respectively) than the PEx group did. Our study demonstrated an acute ergogenic effect of caffeine in an animal weight-lifting exercise model for the first time, suggesting potential avenues for future research.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".