Alcohol use and injury risk in Thailand: A case‐crossover emergency department study
Bibliographic record
Abstract
INTRODUCTION AND AIMS: While injuries and alcohol contribute to a large proportion of the disease burden in Thailand, no well-designed underlying study has yet been published. This study aims to evaluate the relationship between acute alcohol consumption and injury risk in Thailand. DESIGN AND METHODS: Using the case-crossover design, this study examined 520 injured patients aged 18 years and older from two emergency departments in Meuang District, Chiang-Mai Province, Thailand, from June to August of 2016. The case period was defined as 6 h prior to injury, the two control periods as the same 6-h period at 1 day and 7 days prior to injury. Alcohol exposure and the amount consumed were measured for these periods. RESULTS: Twenty percent of injured patients consumed alcohol within the 6 h prior to injury, averaging 6.9 drinks during that time. The odds of injury for those individuals consuming alcoholic beverages was 5.0 (95% confidence interval 3.0, 8.2) times greater compared to non-exposure individuals; every additional drink consumed increased the odds of injury by 1.3 (95% confidence interval 1.2, 1.4). Alcohol use significantly increased the odds of sustaining an unintentional injury, intentional injury inflicted by someone else or experiencing a road traffic injury (among drivers). The dose-response analysis indicated alcohol use significantly increased the risks of unintentional injury and road traffic injuries (among drivers). DISCUSSION AND CONCLUSIONS: Exposure to alcohol increased the odds of injury in a dose-dependent fashion; hence, comprehensive, cost-effective strategies should be implemented in Thailand to reduce alcohol exposure, binge drinking and drunk driving.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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".