Comparison of fructose and glucose ingestion before and during endurance cycling to exhaustion.
Bibliographic record
Abstract
BACKGROUND: Pre-exercise and exercise ingestion of fructose and glucose during cycling exercise were compared. EXPERIMENTAL DESIGN: Seventeen trained subjects ingested a placebo prior to and during a cycling test to exhaustion at 75% VO2max (control group = CG). One week later, subjects were matched on exercise time to exhaustion (ETE) and assigned to a fructose group (FG) or a glucose group (GG). Subjects then performed a second cycling test to exhaustion, ingesting fructose or glucose doses. For all groups (CG, FG and GG), blood was drawn before and at timed intervals during exercise to determine glucose, lactate and free fatty acid (FFA) levels. RESULTS: The ETE for CG was less than either FG (p<0.02) or GG (p<0.001) but FG and GG were similar. FG and GG did not show any differences in blood lactate or blood FFA during the ETE. However, CG FFA levels were higher than those of FG (p<0.02) prior to exercise. CONCLUSIONS: This study demonstrated that fructose and glucose are of equal value in prolonging ETE in endurance cycling Ingesting fructose before and during exercise apparently provided a more constant supply of glucose to be available to the working muscles. The more stable blood glucose levels with fructose ingestion may be beneficial in reducing perceived exhaustion, and thereby allowing for an enhancement in exercise performance.
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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) | 0.000 | 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".