Opium Consumption and the Risk of Traffic Injuries in Regular Users: A Case-Crossover Study in an Emergency Department
Bibliographic record
Abstract
OBJECTIVE: The cause-specific annual death rate due to traffic injuries is around 30 in 100,000 in Iran. On the other hand, this country has the highest proportion of opiate users in the world. Little is known about the transient effect of opium on traffic injuries. The objective of this study was to explore the effect of opium consumption on traffic injuries in drivers who use opium. METHODS: Seventy-five regular opium users who suffered traffic injuries were studied in a case-crossover investigation. The study subjects had been admitted to the single trauma emergency department in Kerman, a city in southeast Iran. The relative risk (RR) of short-term opium effect was estimated by considering frequency of driving after opium consumption during 6 hours before the accident in comparison to the usual frequency of driving after opium consumption by the same persons. Stratified data analysis was performed by the Mantel-Haenszel method. RESULTS: The opium consumption of drivers up to 6 hours before the accident was associated with an increased RR = 3.2, 95 percent confidence interval (CI): 1.9, 5.4. The third hour after consumption had the greatest magnitude of effect considering RR = 4.29, 95 percent CI:2.65, 6.95. CONCLUSIONS: These results suggest a heightened risk of traffic injuries after opium consumption in regular users. The RR in the third hour after consumption could be explained by considering the greater probability of driving compared to the immediate hours after use, rather than peak effect time of opiates. The results indicate necessity of regular assessment of all common drivers, especially truck and bus drivers, regarding use of opium.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".