Fuel selection and cycling endurance performance with ingestion of [<sup>13</sup>C]glucose: evidence for a carbohydrate dose response
Bibliographic record
Abstract
Endurance performance and fuel selection while ingesting glucose (15, 30, and 60 g/h) was studied in 12 cyclists during a 2-h constant-load ride [approximately 77% peak O2 uptake] followed by a 20-km time trial. Total fat and carbohydrate (CHO) oxidation and oxidation of exogenous glucose, plasma glucose, glucose released from the liver, and muscle glycogen were computed using indirect respiratory calorimetry and tracer techniques. Relative to placebo (210+/-36 W), glucose ingestion increased the time trial mean power output (%improvement, 90% confidence limits: 7.4, 1.4 to 13.4 for 15 g/h; 8.3, 1.4 to 15.2 for 30 g/h; and 10.7, 1.8 to 19.6 for 60 g/h glucose ingested; effect size=0.46). With 60 g/h glucose, mean power was 2.3, 0.4 to 4.2% higher, and 3.1, 0.5 to 5.7% higher than with 30 and 15 g/h, respectively, suggesting a relationship between the dose of glucose ingested and improvements in endurance performance. Exogenous glucose oxidation increased with ingestion rate (0.17+/-0.04, 0.33+/-0.04, and 0.52+/-0.09 g/min for 15, 30, and 60 g/h glucose), but endogenous CHO oxidation was reduced only with 30 and 60 g/h due to the progressive inhibition of glucose released from the liver (probably related to higher plasma insulin concentration) with increasing ingestion rate without evidence for muscle glycogen sparing. Thus ingestion of glucose at low rates improved cycling time trial performance in a dose-dependent manner. This was associated with a small increase in CHO oxidation without any reduction in muscle glycogen utilization.
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 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".