Drugs of Misuse: Focus on Vascular Dysfunction
Bibliographic record
Abstract
Common drugs of misuse, including cannabis, opioids, stimulants, alcohol, and anabolic steroids, have strikingly disparate acute and chronic vascular effects, leading to a wide range of clinical cardiovascular presentations. Acute cannabis smoking has been associated with increased risk for myocardial infarction and ischemic stroke in otherwise healthy young people. However, it remains uncertain if people who exclusively smoke cannabis have increased risk for accelerated atherosclerosis similar to that found in people who exclusively smoke tobacco cigarettes. Cocaine and methamphetamines, both stimulants, increase risk for stroke, myocardial infarction, aortic dissection, and accelerated atherosclerosis, but only methamphetamine use is strongly linked to pulmonary hypertension. Chronic alcohol use is strongly associated with chronic hypertension and hemorrhagic stroke, but perhaps confers a lower risk for myocardial infarction. Finally, anabolic steroid use, presumably through adverse effects on circulating lipids and the hematopoietic system, is associated with increased risk for accelerated atherosclerosis and myocardial infarction. Physicians, especially cardiologists, emergency medicine, and internal medicine physicians, should be familiar with the short- and long-term vascular consequences of use of these substances, thereby ensuring appropriate, specific, and informed counselling and treatment.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".