Acute administration of high doses of taurine does not substantially improve high-intensity running performance and the effect on maximal accumulated oxygen deficit is unclear
Bibliographic record
Abstract
The aim of the present study was to investigate the effects of acute administration of taurine overload on time to exhaustion (TTE) of high-intensity running performance and alternative maximal accumulated oxygen deficit (MAODALT). The study design was a randomized, placebo-controlled, crossover design. Seventeen healthy male volunteers (age: 25 ± 6 years; maximal oxygen uptake: 50.5 ± 7.6 mL·kg(-1)·min(-1)) performed an incremental treadmill-running test until voluntary exhaustion to determine maximal oxygen uptake and exercise intensity at maximal oxygen uptake. Subsequently, participants completed randomly 2 bouts of supramaximal treadmill-running at 110% exercise intensity at maximal oxygen uptake until exhaustion (placebo (6 g dextrose) or taurine (6 g) supplementation), separated by 1 week. MAODALT was determined using a single supramaximal effort by summating the contribution of the phosphagen and glycolytic pathways. When comparing the results of the supramaximal trials (i.e., placebo and taurine conditions) no differences were observed for high-intensity running TTE (237.70 ± 66.00 and 277.30 ± 40.64 s; p = 0.44) and MAODALT (55.77 ± 8.22 and 55.06 ± 7.89 mL·kg(-1); p = 0.61), which seem to indicate trivial and unclear differences using the magnitude-based inferences approach, respectively. In conclusion, acute 6 g taurine supplementation before exercise did not substantially improve high-intensity running performance and showed an unclear effect on MAODALT.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| 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".