Use of Medical Cannabis and its derivatives for the anticonvulsant property in Epileptic patients along with Ethical considerations: a Review
Bibliographic record
Abstract
The present study focused on the use of medicinal cannabis and its derivatives for the treatment of epileptic seizures in children, as well as, adults."Epilepsy" defined as a chronic non-communicable disorder of the brain had shown to affect people of all age groups.There were many conventional treatments discovered up till date to minimize and alleviate the symptoms produced as a result of epilepsy, yet none among them could provide the long term safety and efficacy of the interventional drug.On the other hand, increased resistance to conventional drug therapy is another issue which has made researchers to think for the alternative treatment for the long run.On an average, it had been estimated that approximately, 50 million people worldwide were suffering from epilepsy, which thus made it one of the most common neurological diseases globally.Epilepsy accounted for 0. 75%, of the global burden of disease due to premature mortality, lost work productivity, health care needs and unhealthy quality of life.In 2012, epilepsy was responsible for approximately 20. 6 million disability-adjusted life years (DALYs) lost.According to WHO survey, it was estimated that about 80% of the people suffering from epilepsy resided in low-and middleincome countries in which three fourths of people could not access the treatment due to costeffective or non-availability of the treatment in comparison to the people who availed the conventional treatment responded to only approximately 70% of the time.Besides, in many parts of the world, people with epilepsy and their families suffered a lot from stigma and discrimination therefore, making it a social economic burden for hampering the quality of life of the patient as well as, that of the patient caregivers.Several countries (including Canada, Netherlands, and Israel) and 23 of 50 states in the United States had permitted the use of cannabis for medicinal purposes, with or without undergoing a systematic medicines approval process.Epilepsy-related deaths are a significant public health problem, yet are poorly understood and often overlooked.Hence, people with epilepsy are two to three times more likely to die prematurely than people without the disease which is surely a matter of concern to both the health providers and the patient.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".