Effect of chronic marijuana use on cognition: A crosssectional study in urban Bengaluru
Bibliographic record
Abstract
Background and Aim Cannabis or Marijuana is a psychoactive drug. It is one of the commonest recreational drug and the most common illegal drug across the globe. Aim of this study is to assess the cognitive effects of chronic cannabis use using the standardized Montreal Cognitive Assessment MoCA Test and contrast the sociodemographic profile between users and non-users.Materials and methods This was a cross-sectional study conducted in Urban Bengaluru India between July and September 2016. Male and female students aged between 18 and 25 years were included and grouped into Chronic cannabis user or a Control. The MoCA Test was then administered in the subjectrsquos preferred language and assessed based on the defined MoCA administration and grading standards.Results A total of 62 individuals were recruited. 31 individuals were grouped as cases and a matching 31 individualsrsquo controls. Each group comprised on 29 males and 2 females. There was a significant difference between the total score of the MoCA test in the chronic marijuana user group Median 25 when compared to the control group Median 28 U 181 p lt 0.001. Among the different categories of the MoCA tool there was a significant difference between the userrsquos group and the control group in the categories of Visuospatial function Attention and Language.Conclusion Chronic cannabis users were found to have cognitive impairment than those who have never used cannabis.
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".