The ingestion of protein with a maltodextrin and fructose beverage on substrate utilisation and exercise performance
Bibliographic record
Abstract
The study investigated the ingestion of maltodextrin, fructose, and protein on exogenous carbohydrate oxidation (CHOEXO) and exercise performance. Seven trained cyclists and (or) triathletes (maximal oxygen consumption, 59.20 ± 9.00 mL · kg(-1) · min(-1)) performed 3 exercise trials that consisted of 150 min of cycling at 50% maximal power output (160 ± 11 W), followed by a 60-km time trial. One of 3 beverages were randomly assigned during each trial and consumed at 15-min intervals: (i) 0.84 g · min(-1) maltodextrin + 0.52 g · min(-1) fructose + 0.34 g · min(-1) protein (MD+F+P); (ii) 1.10 g · min(-1) maltodextrin + 0.60 g · min(-1) fructose (MD+F); or (iii) 1.70 g · min(-1) maltodextrin (MD). CHO(EXO) and fuel utilisation were assessed via measurement of expired air (13)C content and indirect calorimetry, respectively. Mean total CHO oxidation (CHOTOT) rates were 2.35 ± 0.18, 2.76 ± 0.08, and 2.61 ± 0.17 g · min(-1) with MD, MD+F, and MD+F+P, respectively, although not significantly different. Peak CHO(EXO) rates with MD+F were significantly greater by 41.4% (p = 0.001) and 45.4% (p = 0.0001) compared with MD+F+P and MD, respectively (1.57 ± 0.22 g · min(-1), 1.11 ± 0.08 g · min(-1), and 1.08 ± 0.11 g · min(-1), respectively). Performance times were 2.2% and 5.0% faster with MD+F compared with MD+F+P and MD, respectively; however, they were not statistically significant. Ingestion of an MD-fructose-protein commercial sports beverage significantly reduced peak and mean CHO(EXO) rates compared with MD+F, but did not significantly influence CHOTOT. The addition of protein to an MD+F beverage did not enhance performance times.
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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) | 0.000 | 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) | 0.001 | 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".