Human Immunodeficiency Virus, Hepatitis B, and Hepatitis C Seroprevalence in a Canadian Trauma Population
Bibliographic record
Abstract
BACKGROUND: The current seroprevalence of human immunodeficiency virus (HIV), hepatitis B, and hepatitis C in the Canadian trauma population is unknown. Establishing the seroprevalence of these diseases is vital for education, postexposure prophylaxis, and counseling, and to establish potential screening guidelines. The purpose of this study was to determine the seroprevalence of HIV, hepatitis B, and hepatitis C in the trauma population of London, Ontario, Canada. METHODS: All adult (aged > or = 18 years) trauma patients treated by the trauma team at London Health Sciences Centre were prospectively studied from January to December 2003. The study was conducted as a linked, confidential serosurvey with delayed full disclosure. Serum was analyzed for HIV, hepatitis C antibody, and Hepatitis B surface antigen. RESULTS: A total of 287 (76%) of 377 consecutive trauma patients had blood testing completed. Of the 287 patients tested, 1 (0.3%) was positive for hepatitis B, 8 (2.8%) were positive for hepatitis C, and no patients tested positive for HIV. Hepatitis C-positive patients were predominantly men (63%) with a mean age of 46 years and a mean Injury Severity Score of 19; 63% were injured in a motor vehicle crash, and 88% were discharged alive. There were no statistically significant differences in the demographic and injury profiles from the hepatitis C-negative patients (p > 0.2 for all). CONCLUSION: This is the first study to determine the rates of HIV, hepatitis B, and hepatitis C in the Canadian trauma population. Our trauma population demonstrated a threefold higher hepatitis C seroprevalence rate compared with the general population. Hepatitis C poses the highest risk to the trauma team of the three bloodborne diseases studied. With no vaccine or postexposure prophylaxis currently available for hepatitis C, this study highlights the importance of prevention and the strict use of universal precautions in the setting of trauma.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".