The Use of Medical Cannabis on Cancer in Thailand
Bibliographic record
Abstract
The objective of this paper is to indicate the beneficial utility of medical marijuana. Marijuana throughout history is known for its property to alternate consciousness. However, the medical utilization of marijuana or cannabis was dated as far back as 2900 BC, when it was used by Emperor Ru Hsi of Ancient Chinese. During the 19th century, marijuana was introduced to Western Medicine as a therapeutic drug, mostly known for its pain control properties. Marijuana by itself consists of more than 100 active components. In consideration of the amount of THC, tetrahydrocannabinol, a psychological chemical released by the glands of marijuana plants, CBD or cannabidiol, amongst the most prevalent ingredients in cannabis, is the least controversial extract extracted from the marijuana plants to be used. As of the year 2019, Thailand Narcotics Act legalized cannabis for medical use in Thailand. A study survey conducted by N.Z. shows that in just over a year, 20% of the patients report the use of cannabis for medical purposes regarding its benefits of neuropathic pain, chemotherapy-induced nausea and vomiting, Aids-related cachexia, intractable epilepsy, and palliative care conditions. Further clinical trials are conducted to further perceive the potential cannabis has on treating cancer. One of the two successful clinical trials that have been published proposes that cannabis may make it possible to treat brain cancer with few side effects. Keywords: Cancer, Marijuana, Tetrahydrocannabinol (THC), Cannabidiol (CBD), Medical Usage, Cannabis in Thailand.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".