Blood-borne virus testing in European emergency departments: current evidence and service considerations
Bibliographic record
Abstract
Innovative testing approaches are needed to meet global targets for the blood-borne viruses (BBVs) HIV, hepatitis B virus (HBV) and hepatitis C virus (HCV). We conducted a systematic review of BBV testing in emergency departments (EDs) in Europe to evaluate prevalence, effectiveness of ED testing and linkage to care (LTC). We searched PubMed, Embase and Cochrane Library for articles on ED BBV testing published between January 2012 and July 2022. Studies conducted outside Europe or prior to 2012 were excluded owing to epidemiological and healthcare service variation, together with studies that did not report core parameters. Reference lists from included articles were manually searched. Seventeen original articles met the inclusion criteria. Seven studies reported on HIV testing only. ED prevalence: HIV Ab, 0.0%-1.1%; HBsAg, 0.2%-0.9%; and HCV RNA, 0.2%-3.9%. BBV testing uptake varied by policy and offer methodology: opt-out, provider-initiated: 9.7%-44.2%; electronic health record (EHR) modification: 52.1%-88.9%; and opt-in, provider-initiated: 3.9%-37.7%. LTC rates were 8.1%-100% and varied by BBV, generally highest for HIV and lowest for HCV. There was variable detail in outcome reporting and description of clinical LTC pathways. ED BBV testing in Europe is feasible and identifies high numbers of infections (including, where reported, new diagnoses and disengaged patients), often among marginalized populations who use open-access EDs for healthcare. Factors associated with higher levels of sustained testing uptake included opt-out testing (vs opt-in), EHR (vs provider-initiated) and integration of community services. We propose a toolkit of components necessary for a high-performing ED BBV testing programme.
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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 | 0.027 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".