Effects of two different doses of carbohydrate ingestion on taekwondo-related performance during a simulated tournament
Bibliographic record
Abstract
BACKGROUND: Carbohydrate (CHO) ingestion enhances exercise performance; however, the efficacy of CHO intake on repeated bouts of exercise simulating a taekwondo tournament is unknown. Therefore, the purpose was to compare the effects of two different doses of CHO on a sports-specific kicking test during a simulated taekwondo tournament compared to placebo (PLA). METHODS: ; both solutions containing 2:1 glucose:fructose), or a PLA immediately following each kicking test. The kicking test was repeated 5 times, separated by 45 mins of rest, simulating a typical taekwondo competition day. Ratings of perceived exertion (RPE) and gastrointestinal discomfort (GI) scores were collected immediately after, and blood glucose before each test. RESULTS: The results revealed that C45 and C22.5 did not improve total, successful, or percentage of successful kicks compared to PLA (p > 0.05). Blood glucose was significantly higher following both CHO conditions compared with PLA across all five tests (p < 0.05). There were no differences between treatments or across tests for RPE (p > 0.05). CONCLUSION: CHO intake, independent of the dose, did not alter taekwondo kick performance during a simulated taekwondo tournament.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".