Increased Incidence of Complications in Trauma Patients Cointoxicated With Alcohol and Other Drugs
Bibliographic record
Abstract
BACKGROUND: Alcohol and drug intoxication is prevalent in trauma patients. Although intoxication and cointoxication can have a range of physiologic effects, their implications for clinical management are unclear. The current investigation aims to assess the effects of alcohol and substance use as well as the interaction between these two states on outcomes and in-hospital complications. METHODS: All trauma patients with an Injury Severity Score (ISS) >or=12 during a 5-year period who were tested for both alcohol and other drugs were included. Alcohol-positive, drug-positive, and both-positive patients were compared with patients who tested negative. Logistic regression analysis was performed controlling for age and ISS to assess the relative contribution of intoxication or cointoxication in determining clinical outcomes and in-hospital complications. RESULTS: For alcohol-positive and drug-positive patients, intoxication status did not appear to influence outcomes. However, cointoxicated individuals were found to have an increased incidence of complications overall (odds ratio [OR] = 2.06), an increased incidence of pneumonia specifically (OR = 3.34) and an increased incidence of the requirement for mechanical ventilation (OR = 2.37). CONCLUSIONS: Cointoxication with alcohol and other drugs is a risk factor for increased in-hospital complications.
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".