Complementary and alternative medicine (CAM) for epilepsy treatment in the Middle East and North Africa (MENA) region
Bibliographic record
Abstract
INTRODUCTION: The aim of this study is to provide the reader with a review on Complementary and Alternative Medicine (CAM) treatment in epilepsy in the Middle East and North Africa (MENA) region, to describe the extent and factors associated with its use among patients with epilepsy (PWE), and to recommend how effectively we will be able to reduce this alarming use. MATERIAL AND METHODS: Retrospective literature search from 1945 to December 2019, regarding CAM use in the MENA region, using electronic databases (PubMed, Scopus, Google Scholar, Web of Science). CONCLUSION: The use of CAM and consultation of traditional healers for the treatment of epilepsy has so far been widespread practice for centuries in the MENA region. Lack of health professionals and non-adherence to conventional epilepsy treatment are strongly associated with the use of CAM. Improvement in the level of knowledge of epilepsy among PWE, healthcare professionals, including traditional healers, will educate PWE and their caregivers on potentially unsafe practices and promote adherence to Antiseizure Drugs (ASDs). Additionally, randomized controlled trials are needed to study the role and value of various CAM treatment options in PWEs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".