Bibliographic record
Abstract
Caffeine is known to prolong exercise times during a single bout of exhaustive exercise yet the effect on performance with repeated dosing of the drug has not been determined. PURPOSE To examine the effect of repeated doses of caffeine on exercise performance. METHOD Nine male caffeine users performed exercise rides (ER) to exhaustion at 80% maximal aerobic capacity after ingesting a placebo, 5 mg/kg of caffeine or 2.5 mg/kg of caffeine one hour before the ER. Two ERs were performed weekly on the same day once in the morning (AM) and five hours later in the afternoon (PM). There were 4 treatments containing either caffeine or placebo (a dietary fibre) i.e., trial A representing 5 mg/kg caffeine in the AM and 2.5 mg/kg caffeine in the PM; trial B which was placebo in both AM and PM; trial C representing 5 mg/kg caffeine in the AM and placebo in the PM; and trial D representing a placebo in the AM and 5 mg/kg caffeine in the PM. The order of the treatment trials was double-blind and randomized. RESULTS Caffeine ingestion significantly increased exercise time to exhaustion in the AM (trial A 24.9 ± 10.2 min and trial C 21.8 ± 4.9 versus trial B 18.0 ± 6.4 min and D 17.7 ± 4.3 min). This effect was maintained in the PM and greater than placebo (B 18.3 ± 4.8 min) regardless of whether redosing (trial A 21.5 ± 8.6 min) or placebo (trial C 21.0 ± 6.8) followed the initial morning dose. Caffeine dosing in the PM (trial D 22.4 ± 7.2 min) also increased ER following placebo trial D in the AM. CONCLUSIONS Redosing with caffeine following exhaustive exercise in the AM was not necessary to maintain the ergogenic effect of the drug during subsequent exercise performed 5 hours later in the PM.
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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 | 0.002 |
| 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 | 0.001 |
| 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".