ALCOHOL AND INJURY: MULTI-LEVEL ANALYSIS FROM THE EMERGENCY ROOM COLLABORATIVE ALCOHOL ANALYSIS PROJECT (ERCAAP)
Bibliographic record
Abstract
AIMS: To analyze the relationship between individual-level characteristics and site-level contextual variables on the association of acute alcohol use and injury. METHODS: Blood alcohol concentration (BAC) and survey data collected (using similar methodology and instruments) at the time of the emergency department (ED) visit, between 1985 and 2003 on probability samples of injured and non-injured patients (n = 18 438) from 31 EDs in seven countries (Argentina, Canada, Italy, Mexico, Poland, Spain, USA) were analyzed using hierarchical linear modeling (HLM). RESULTS: BAC and self-reported consumption were predictive of an injury (compared to a non-injury), controlling for gender and age, with odds ratios of 1.51 and 1.58, respectively. The likelihood of injury given a positive BAC and self-report was less for heavier drinkers (those reporting five or more drinks on an occasion) than for lighter drinkers, and was greater in those societies with greater detrimental drinking patterns than those with lower detrimental patterns. CONCLUSIONS: These data suggest a moderate, but robust association of a positive BAC and self-report with admission to the ED for an injury, which is modified by the patient's usual heavier drinking and by societal drinking patterns.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".