Alcohol use among fatally injured victims in São Paulo, Brazil: bridging the gap between research and health services in developing countries
Bibliographic record
Abstract
BACKGROUND AND AIMS: Most studies reporting alcohol use among fatally injured victims are subject to bias, particularly those related to sample selection and to absence of injury context data. We developed a research method to estimate the prevalence of alcohol consumption and test correlates of alcohol use prior to fatal injuries. DESIGN, SETTING AND PARTICIPANTS: Cross-sectional study based on a probability sample of fatally injured adult victims (n = 365) autopsied in São Paulo, Brazil. Victims were sampled within systematically selected 8-hour sampling blocks, generating a representative sample of fatal injuries occurring during all hours of the day for each day of the week between June 2014 and December 2015. MEASUREMENTS: The presence of alcohol and blood alcohol concentration (BAC) were the primary outcomes evaluated according to victims' socio-demographic, injury context data (type, day, time and injury place) and criminal history characteristics. FINDINGS: Alcohol was detected in 30.1% [95% confidence interval (CI) = 25.6-35.1)] of the victims, with a mean blood alcohol level (BAC) level of 0.11% w/v (95% CI = 0.09-0.13) among alcohol-positive cases. Black and mixed race victims presented a higher mean BAC than white victims (P = 0.03). Fewer than one in every six suicides tested positive for alcohol, while almost half of traffic-related casualties were alcohol-positive. Having suffered traffic-related injuries, particularly those involving vehicle crashes, and injuries occurring during weekends and at night were associated significantly with alcohol use before injury (P < 0.05). CONCLUSIONS: Nearly one-third of fatal injuries in São Paulo between June 2014 and December 2015 were alcohol-related, with traffic accidents showing a greater association with alcohol use than other injuries. The sampling methodology tested here, including the possibility of adding injury context data to improve population-based estimates of alcohol use before fatal injury, appears to be a reliable and lower-cost strategy for avoiding biases common in death investigations.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".