Alcohol and cannabis use as risk factors for injury – a case-crossover analysis in a Swiss hospital emergency department
Bibliographic record
Abstract
BACKGROUND: There is sufficient and consistent evidence that alcohol use is a causal risk factor for injury. For cannabis use, however, there is conflicting evidence; a detrimental dose-response effect of cannabis use on psychomotor and other relevant skills has been found in experimental laboratory studies, while a protective effect of cannabis use has also been found in epidemiological studies. METHODS: Implementation of a case-crossover design study, with a representative sample of injured patients (N = 486; 332 men; 154 women) from the Emergency Department (ED) of the Lausanne University Hospital, which received treatment for different categories of injuries of varying aetiology. RESULTS: Alcohol use in the six hours prior to injury was associated with a relative risk of 3.00 (C.I.: 1.78, 5.04) compared with no alcohol use, a dose-response relationship also was found. Cannabis use was inversely related to risk of injury (RR: 0.33; C.I.: 0.12, 0.92), also in a dose-response like manner. However, the sample size for people who had used cannabis was small. Simultaneous use of alcohol and cannabis did not show significantly elevated risk. CONCLUSION: The most surprising result of our study was the inverse relationship between cannabis use and injury. Possible explanations and underlying mechanisms, such as use in safer environments or more compensatory behavior among cannabis users, were discussed.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".