A Ten year review of alcohol use and major trauma in a Canadian province: still a major problem
Bibliographic record
Abstract
BACKGROUND: Alcohol plays a significant role in major traumatic injuries. While the role of alcohol in motor vehicle trauma (MVT) is well described, its role and approaches to prevention in other injury mechanisms is less defined. METHODS: A 10 year retrospective examination of Alberta Trauma Registry (ATR) data was conducted on all major trauma patients (age ≥ 9 and ISS ≥ 12) from 2001-2010. The role and prevalence of alcohol is examined. RESULTS: Of 22,457 patients included in our study, only 60 %(n = 13,552) were screened for alcohol use. Of those screened, 38 %(n = 5,170) tested positive for alcohol with a mean blood alcohol concentration (BAC) of 39.4 ± 21.1 mmol/L. Of the positive screening tests, 82.3 % had BAC levels greater than the common legal driving limit of 17.4 mmol/L (0.08 %). Testing positive was associated with male gender (p < 0.001) and younger age (p < 0.001). The rate of positive alcohol use in major trauma increased from 20.3 % in 2001 to 24.3 % in 2010, corresponding with a screening rate increase from 51.3 % to 61.2 % over the same period. Railway incidents have the highest rate of alcohol involvement (65 %), followed by undetermined-if-accidental/self-inflicted (53.5 %) and assault (49 %); motor vehicle traffic (MVT) incidents had a frequency of 25.4 %. CONCLUSIONS: The prevalence of alcohol use in major trauma appears to be increasing in Alberta but the true extent is still underappreciated. Furthermore, the role of alcohol in non-MVT injuries is significant and deserves further attention. The vast majority of patients involved in alcohol-related trauma are legally intoxicated. Alcohol use continues to be a substantial contributor to major trauma in Alberta, and represents an important opportunity to reduce preventable injuries.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.022 | 0.044 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".