Energy Drinks: A Contemporary Issues Paper
Bibliographic record
Abstract
Since their introduction in 1987, energy drinks have become increasingly popular and the energy drink market has grown at record pace into a multibillion-dollar global industry. Young people, students, office workers, athletes, weekend warriors, and service members frequently consume energy drinks. Both health care providers and consumers must recognize the difference between energy drinks, traditional beverages (e.g., coffee, tea, soft drinks/sodas, juices, or flavored water), and sports drinks. The research about energy drinks safety and efficacy is often contradictory, given the disparate protocols and types of products consumed: this makes it difficult to draw firm conclusions. Also, much of the available literature is industry-sponsored. After reports of adverse events associated with energy drink consumption, concerns including trouble sleeping, anxiety, cardiovascular events, seizures, and even death, have been raised about their safety. This article will focus on energy drinks, their ingredients, side effects associated with their consumption, and suggested recommendations, which call for education, regulatory actions, changes in marketing, and additional research.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.058 | 0.018 |
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".