Energy Drinks and Their Adverse Health Effects: A Systematic Review and Meta-analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
CONTEXT: Energy drinks are the fastest growing product in the beverage industry. However, there is concern regarding potential for adverse effects with use. OBJECTIVE: To evaluate the reported adverse effects of energy drink consumption. DATA SOURCES: The electronic databases MEDLINE, EMBASE, and PubMed were searched for relevant studies from inception to November 2019, and pertinent data were abstracted. STUDY SELECTION: Only clinical studies reporting adverse events after energy drink consumption were included. STUDY DESIGN: Systematic review. LEVEL OF EVIDENCE: Level 4. DATA EXTRACTION: Data regarding sample size characteristics, energy drink characteristics, comparators, and all adverse events were extracted in duplicate and recorded. RESULTS: A total of 32 studies and 96,549 individuals were included. Frequently reported adverse events in the pediatric population were insomnia (35.4%), stress (35.4%), and depressive mood (23.1%). Frequently reported adverse events in the adult population were insomnia (24.7%), jitteriness/restlessness/shaking hands (29.8%), and gastrointestinal upset (21.6%). Alcohol mixed with energy drinks significantly reduced the likelihood of sedation effects but increased the likelihood of stimulatory effects. Energy drink consumption significantly increased the odds of insomnia (OR, 5.02; 95% CI, 1.72-14.63) and jitteriness/activeness (OR, 3.52; 95% CI, 1.28-9.67) compared with the control group. CONCLUSION: The authors recommend that individuals avoid frequent energy drink consumption (5-7 energy drinks/week) and avoid co-consumption with alcohol; increased regulatory standards should be placed in the sale of energy drinks, particularly with regard to the pediatric population.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.024 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it