Retrospective analysis of alcohol testing in trauma team activation patients at a Canadian tertiary trauma centre
Bibliographic record
Abstract
OBJECTIVES: Although alcohol screening is an essential requirement of level I trauma centre accreditation, actual rates of compliance with mandatory alcohol testing in trauma patients are seldom reported. Our objective was to determine the prevalence of blood alcohol concentration (BAC) testing in patients requiring trauma team activation (TTA) for whom blood alcohol testing was mandatory, and to elucidate patient-level, injury-level and system-level factors associated with BAC testing. DESIGN: Retrospective cohort study. SETTING: Tertiary trauma centre in Halifax, Canada. PARTICIPANTS: 2306 trauma patients who required activation of the trauma team. PRIMARY OUTCOME MEASURE: The primary outcome was the rate of BAC testing among TTA patients. Trends in BAC testing over time and across patient and injury characteristics were described. Multivariable logistic regression examined patient-level, injury-level and system-level factors associated with testing. RESULTS: Overall, 61% of TTA patients received BAC testing despite existence of a mandatory testing protocol. Rates of BAC testing rose steadily over the study period from 33% in 2000 to 85% in 2010. Testing varied considerably across patient-level, injury-level and system-level characteristics. Key factors associated with testing were male gender, younger age, lower Injury Severity Score, scene Glasgow Coma Scale score <9, direct transport to hospital and presentation between midnight and 09:00 hours, or on the weekend. CONCLUSIONS: At this tertiary trauma centre with a policy of empirical alcohol testing for TTA patients, BAC testing rates varied significantly over the 11-year study period and distinct factors were associated with alcohol testing in TTA patients.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".