Bibliographic record
Abstract
BACKGROUND AND PURPOSE: An increasing number of case reports link cannabis consumption to cerebrovascular events. Yet these case reports have not been scrutinized using criteria for causal inference. METHODS: All case reports on cannabis and cerebrovascular events were retrieved. Four causality criteria were addressed: temporality, adequacy of stroke work-up, effects of rechallenge, and concomitant risk factors that could account for the cerebrovascular event. RESULTS: There were 34 case reports on 64 patients. Most cases (81%) exhibited a temporal relationship between cannabis exposure and the index event. In 70%, the evaluation was sufficiently comprehensive to exclude other sources for stroke. About a quarter (22%) of patients had another stroke after subsequent re-exposure to cannabis. Finally, half of patients (50%) had concomitant stroke risk factors, most commonly tobacco (34%) and alcohol (11%) consumption. CONCLUSIONS: Many case reports support a causal link between cannabis and cerebrovascular events. This accords well with epidemiological and mechanistic research on the cerebrovascular effects of 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".