Nitrate supplementation and high-intensity performance in competitive cyclists
Bibliographic record
Abstract
Consumption of inorganic nitrate (NO3(-)) is known to enhance endurance exercise performance in recreationally trained subjects. Here we report the effect on a high-intensity performance task in national-level cyclists. The performance test consisted of 2 cycle ergometer time trials of 4 min duration with 75 min between trials. In a randomized crossover design, 26 cyclists performed the test under the following 4 conditions (each separated by a 6-day washout): consumption of 70 mL of nitrate-rich beetroot juice at 150 min or 75 min before the first time trial, addition of a 35 mL "top-up dose" following the first time trial in the 150 min condition, and consumption of a placebo. A linear mixed model with adjustments for learning effects and athlete fitness (peak incremental power) was used to estimate effects on mean power, with probabilistic inferences based on a smallest important effect of 1.0%. Peak plasma nitrite (NO2(-)) concentration was greatest when nitrate was taken 75 min before the first time trial. Relative to placebo, the mean effect of all 3 nitrate treatments was unclear in the first time trial (1.3%, 90% confidence limits: ±1.7%), but possibly harmful in the second time trial (-0.3%, ±1.6%). Differences between nitrate treatments were unclear, as was the estimate of any consistent individual response to the treatments. Allowing for sampling uncertainty, the effect of nitrate on performance was less than previous studies. Under the conditions of our experiment, nitrate supplementation may be ineffective in facilitating high-intensity exercise in competitive athletes.
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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) | 0.001 | 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".