Caffeine, Coffee and Ephedrine: Impact on Exercise Performance and Metabolism
Bibliographic record
Abstract
This paper addresses areas where there is controversy regarding caffeine as an ergogenic aid and also identifies topics that have not been adequately addressed. It is clear that caffeine, in moderate amounts, can be used orally as an ergogenic aid in aerobic activity lasting for more than 1 min. It increases endurance and speed, but not maximal VO2 and related parameters. While there are fewer well-controlled studies for resistance exercise, the literature would suggest similar improvements: increased endurance at submaximal tension and power generated in repeated contractions and no change in maximal ability to produce force. It is likely that theophylline (a related methylxanthine) has similar actions and it has been suggested that the combination of caffeine and sympathomimetics may be a more potent erogenic aid. The voids in our understanding of caffeine include the dose (what amount is optimal, what vehicle is used to deliver the drug as well as method, pattern, and mode of administration), the potential side effects (particularly in competitive settings), health implications (insulin resistance and if combined with ephedrine, cardiovascular risks) and mechanisms of action. It appears unlikely that increased fat oxidation and glycogen sparing is the prime ergogenic mechanism.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".