Perceptions and Realities of Testing for Alcohol and Other Drugs in Trauma Patients
Bibliographic record
Abstract
BACKGROUND: Routine testing for acute intoxication with alcohol and other drugs in trauma patients is a widely accepted practice at major trauma centers. However, there is often a significant difference between policy and practice. We sought to study the perception and the reality of testing practices at an urban Level I trauma center. METHODS: A survey was distributed to all emergency nursing staff and trauma team leaders (TTL). For the empiric aspects of this study, data were obtained from an institutional trauma database. Descriptive statistics and bivariate odds ratios were used to characterize survey responses and empiric data, respectively. RESULTS: Eighty-nine percent of nurses and 80% of TTLs estimated testing rates for alcohol to be over 80% for all trauma patients. This was compared with the empirically determined rate of 65%. Over 90% of nurses and 70% of TTLs claimed to test all patients. Those more likely to be tested were younger, male, unemployed, or laborers with penetrating injuries, more commonly in the overnight period. Clinicians tended to rely on clinical suspicion of intoxication as a criterion to test. They also tended to be unaware of an association between low socioeconomic status and increased rates of testing in their practices. CONCLUSIONS: Our results suggest that clinicians generally support the notion of routine testing for trauma patients; however, they tend to overestimate the rates at which these tests are performed. Additionally, clinicians demonstrate testing biases that may influence the distribution of care in the acutely injured population.
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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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".