Energy drinks: What is all the hype? The dangers of energy drink consumption
Bibliographic record
Abstract
PURPOSE: To describe the adverse effects associated with energy drink consumption among adolescents and young adults. DATA SOURCES: Review of literature utilizing Medscape, the Internet, MD Consult, and CINAHL. The following search terms were used: Energy drinks, caffeine, guarana, taurine, ginseng, sugar, and caffeine toxicity. Search was limited to English language sources from 2005 to 2010. CONCLUSIONS: The popularity of energy drinks and the rapid growth of their excessive consumption among adolescents and young adults have brought about great concern in regards to overall health and well-being. Caffeine, which is readily available to minors, is the most commonly used psychoactive substance in the world and imposes a potentially harmful influence on health, academic performance, and personal adjustments. Teens and young adults account for nearly $2.3 billion of energy drink sales. Adolescents and young adults are often unaware that various products, such as energy drinks, herbal medications, and various other medications that promote alertness, contain caffeine. When these products are taken together, caffeine toxicity and severe adverse effects can occur. IMPLICATIONS FOR PRACTICE: Practitioners need to be aware of the consequences of energy drink consumption and be prepared to provide appropriate patient education.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".