Adverse Effects of Amphetamines on the Cardiovascular System: Review and Retrospective Analyses of Trends
Bibliographic record
Abstract
BACKGROUND: Amphetamine and amphetamine-type stimulants are powerful physical and psychostimulants; they are phenethylamine derivatives. The use of amphetamines can be either medicinal or illicit. Several amphetamines have been redesigned into illegal drugs of potent properties, also known as research chemicals and designer drugs. Hence, they are named novel (new) psychoactive substances (NPS).MATERIALS & METHODS: This study is a hybrid study of; data crunching and retrospective analysis of a trends database (1), and a systematic review of literature in relation to the amphetamines-induced adverse effects on the cardiovascular system (2). Google Trends database has been analysed in retrospect (2012-2017) to evaluate the attentiveness of surface web users towards amphetamine and a potent renowned amphetamine derivative known as captagon (fenethylline).RESULTS: Amphetamines appear to be highly popular worldwide, particularly in the developed world including North America and European countries, and to a less extent in the developing countries including the Middle East. However, the trends are oscillating with time with significant year-to-year changes although there was some steadiness in the temporal patterns (trends), for example in 2013-2014 (p-value=0.258). Variations in the trends were found to be correlated with global events including international terrorism. The adverse effects of amphetamines were found to be highly related to the cardiovascular system with a high incidence of intoxications and deaths among substance (ab)users.CONCLUSION: Several amphetamines are potent and used illicitly beyond their original therapeutic potential, as in the case of captagon, culminating in monumental public and economic threats. Legalising bodies should exercise tremendous and systematic efforts to counteract these threats. Database analyses can provide an accurate insight into this phenomenon that has been growing exponentially in the past decade.
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 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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".