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 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 | 0.001 |
| 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".