Does a physiological concentration of taurine increase acute muscle power output, time to fatigue, and recovery in isolated mouse soleus (slow) muscle with or without the presence of caffeine?
Bibliographic record
Abstract
High concentrations of caffeine and taurine are key constituents of many ergogenic supplements ingested acutely to provide legal enhancements in athlete performance. Despite this, there is little evidence supporting the claims for the performance-enhancing effects of acute taurine supplementation. In-vitro models have demonstrated that a caffeine-induced muscle contracture can be further potentiated when combined with a high concentration of taurine. However, the high concentrations of caffeine used in previous research would be toxic for human consumption. Therefore, this study aimed to investigate whether a physiological dose of caffeine and taurine would directly potentiate skeletal muscle performance. Isolated mouse soleus muscle was used to examine the effects of physiological taurine (TAU), caffeine (CAF), and taurine-caffeine combined (TC) on (i) acute muscle power output; (ii) time to fatigue; and (iii) recovery from fatigue, compared with the untreated controls (CON). Treatment with TAU failed to elicit any significant difference in the measured parameters. Treatment with TC resulted in a significant increase in acute muscle power output and faster time to fatigue. The ergogenic benefit posed by TC was not different from the effects of caffeine alone, suggesting no acute ergogenic benefit of taurine.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".