RETROSPECTIVE STUDY ON EFFECTS OF MARIJUANA AND KHAT ON MEMORY AMONG VASCULAR DEMENTIA PATIENTS
Bibliographic record
Abstract
Background Marijuana and khat, as commonly called cannabis and cathine respectively are widely used in Ethiopia. However, there are very limited and conflicting data about their effects on memory of patients with vascular dementia. Besides it is not clear if these recreational addictive substances have improving or worsening impacts on these patients.Objective Assess impacts of Cannabis and Cathine on memory among patients with vascular dementia.Methods We reviewed medical records of 300 patients with vascular dementia in Black Lion Hospital from January 2010 to December 2017 and identified 4 groups (Cannabis and Khat users, Cannabis only, Khat only and non-users) and compared their memory using Montreal cognitive assessment test score done at the beginning and their follow-up visits.ResultsBaseline characteristics, including risk factors for stroke were all comparable except for gender where 216(90%) were male substance abusers.120 (40%) patients use Cannabis and Cathine. 54 (18%) use cannabis only and 66 (22%) use Cathine only. For age, education level matched analysis, baseline and 2 follow up Montreal cognitive assessment test scores demonstrated sharp decline in memory function in patients who abuse cannabis and Khat (OD of 2.31, 95% CI 1.61 - 2.45), moderate decrement in Cannabis users (OD of 1.24, 95% CI 0.93 - 1.45) and no significant change noticed among khat users (OD of 0.86, 95% CI 0.83 - 1.12).CONCLUSION Cannabis plays a major role in deterioration of memory, especially if taken together with Khat among patients with vascular dementia while khat alone has no effect on memory.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".