The Relationship Between Self‐Reported Drinking and BAC Level in Emergency Room Injury Cases: Is it a Straight Line?
Bibliographic record
Abstract
BACKGROUND: While the validity of self-reported consumption based on blood alcohol concentration (BAC) has been found to be high in emergency room (ER) samples, little research exists on the estimated number of drinks consumed given a BAC level. Such data would be useful in establishing a dose-response relationship between drinking and risk (e.g., of injury) in those studies for which the number of drinks consumed is not available but BAC is. METHODS: Several methods were used to estimate the number of drinks consumed in the 6 hours prior to injury based on BAC obtained at the time of ER admission of n = 1,953 patients who self-reported any drinking 6 hours prior to their injury and who arrived to the ER within 6 hours of the event, from the merged Emergency Room Collaborative Alcohol Analysis Project (ERCAAP) and the World Health Organization Collaborative Study on Alcohol and Injury across 16 countries. RESULTS: The relationship between self-reported consumption and averaged BAC within each consumption level appeared to be fairly linear up to about 7 drinks and a BAC of approximately 100 mg/dl. Above about 7 reported drinks, BAC appeared to have no relationship with drinking, possibly representing longer consumption periods than only the 6 hours before injury for those reporting higher quantities consumed. Both the volume estimate from the bivariate BAC to self-report relationship as well as from a Widmark calculation using BAC and time from last drink to arrival to the ER indicated a somewhat weak relationship to actual number of self-reported drinks. CONCLUSIONS: Future studies may benefit from investigating the factors suspected to be driving the weak relationships between these measures, including the actual time over which the reported alcohol was consumed and pattern of drinking over the consumption period.
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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 | 0.003 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".