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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".