The effects of opium on the cardiovascular system: a review of side effects, uses, and potential mechanisms
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
BACKGROUND: In Iran, as in many other Asian and Middle Eastern countries, some believe that opium has beneficial effects on cardiovascular system. Dependent patients suppose that opium has positive effects on cardiovascular function and can prevent or improve cardiovascular diseases; however, only few comprehensive studies evaluating such effects have been performed. OBJECTIVES: In this study, we sought to clarify the effect of opium on cardiovascular problems by incorporating the previous findings and the current information on the issue and to explain the possible mechanisms of this effect. METHODS: The available human studies published up to October 30, 2019, were searched in different databases. Case-control, cohort, and cross-sectional studies were retrieved. Papers published in English or those with an English abstract were included. The risk of bias for each included study was assessed based on the Newcastle-Ottawa Scale (NOS). We then categorized the effects of opium on cardiovascular problems along with its probable underlying mechanisms of action. RESULTS: In this study, most of the published articles suggested the adverse effects of opium on the cardiovascular system, including atherosclerosis, myocardial infarction, arrhythmia, low ejection fraction, and cardiovascular mortality; however, some articles reported the beneficial or impartial effects of opium on the cardiovascular system. In this article, we have categorized all the effects of opium on cardiovascular system; also, the proposed mechanisms of action of opium in each of the above-mentioned disorders are summarized. CONCLUSION: Although the available evidences were incoherent, it was mostly suggested that opium use does not protect against or improve cardiovascular problems.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it