A commentary on the medicinal use of marijuana
Bibliographic record
Abstract
BACKGROUND: Lately, the number of people using marijuana in the USA has dramatically increased. In 2018, many states have legalized marijuana use for both medical and recreational purposes, thus exploring the evidence behind medical marijuana use became essential. Muslim majority countries enforce rigorous rules as marijuana has been a long-debated issue due to the stigma associated with its use as a treatment. Marijuana has a high beneficial effect in managing chronic pain in adults and relieving spasticity symptoms in multiple sclerosis, obstructive sleep apnea syndrome, and fibromyalgia. As well as, used as pain management, and as anti-emetic in treatment of chemotherapy-induced vomiting and nausea. Marijuana is requested from more than one-third of posttraumatic stress disorder patients due to its significant clinical improvement in nightmares and subsidence disorder symptoms.Marijuana adversely affects the body's resistance to many infections, compromising their immune response. Its recreational use has led to an increasing trend in the occurrence of major acute cardiovascular events as stroke, epilepsy, acute myocardial infarction, congestive heart failure, and arrhythmia. CONCLUSION: Many countries started to allow medicinal use of marijuana due to its beneficial effect in managing chronic pain, spasticity symptoms in multiple sclerosis, obstructive sleep apnea syndrome, fibromyalgia, and posttraumatic stress disorder. But literature lacks benefit-harm analysis for marijuana usage in medicine. Therefore, evidence-based report of short- and long-term health effects of marijuana use-both harmful and beneficial effects-is crucial for further marijuana prescription in healthcare settings.
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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 | 0.014 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.065 | 0.062 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".