Common questions and misconceptions about caffeine supplementation: what does the scientific evidence really show?
Bibliographic record
Abstract
Caffeine is a popular ergogenic aid that has a plethora of evidence highlighting its positive effects. A Google Scholar search using the keywords "caffeine" and "exercise" yields over 200,000 results, emphasizing the extensive research on this topic. However, despite the vast amount of available data, it is intriguing that uncertainties persist regarding the effectiveness and safety of caffeine. These include but are not limited to: 1. Does caffeine dehydrate you at rest? 2. Does caffeine dehydrate you during exercise? 3. Does caffeine promote the loss of body fat? 4. Does habitual caffeine consumption influence the performance response to acute caffeine supplementation? 5. Does caffeine affect upper vs. lower body performance/strength differently? 6. Is there a relationship between caffeine and depression? 7. Can too much caffeine kill you? 8. Are there sex differences regarding caffeine's effects? 9. Does caffeine work for everyone? 10. Does caffeine cause heart problems? 11. Does caffeine promote the loss of bone mineral? 12. Should pregnant women avoid caffeine? 13. Is caffeine addictive? 14. Does waiting 1.5-2.0 hours after waking to consume caffeine help you avoid the afternoon "crash?" To answer these questions, we performed an evidence-based scientific evaluation of the literature regarding caffeine supplementation.
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 | 0.204 | 0.498 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".