Cannabis and cognitive functioning in multiple sclerosis: The role of gender
Bibliographic record
Abstract
Background Cognitive function in people with multiple sclerosis (PwMS) is associated with gender differences and the use of smoked/ingested cannabis. Objective The objective of this report is to explore a possible gender-cannabis interaction associated with cognitive dysfunction in PwMS. Methods A retrospective analysis was undertaken of cognitive data collected from 140 PwMS. A general linear model was conducted to determine gender and cannabis effects on processing speed (SDMT), verbal (CVLT-II) and visual (BVMT-R) memory, and executive functions (D-KEFS), while controlling for age and years of education. Results Cannabis was smoked at least once a month by 33 (23.6%) participants. Cannabis users were more impaired on the SDMT ( p = 0.044). Men, who comprised 30.7% of the entire sample and 42.2% of cannabis users, were more impaired on the CVLT-II (total learning, p = 0.001; delayed recall, p = 0.004). A cannabis-gender interaction was found with the CVLT-II delayed recall ( p = 0.049) and BVMT-R total learning ( p = 0.014), where male cannabis users performed more poorly than female. Conclusion Males with MS may be particularly vulnerable to the cognitive side effects of smoked cannabis use.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".