Hepatitis B prevention interventions during HIV post-exposure prophylaxis visits: A retrospective chart review
Bibliographic record
Abstract
BackgroundHepatitis B virus (HBV) disproportionately affects people at risk of HIV. Encounters for HIV post-exposure prophylaxis (PEP) create opportunities for HBV screening and prevention. We quantified HBV prevalence, susceptibility, and active/passive immunization use among patients seeking HIV PEP.MethodsWe conducted a retrospective chart review of patients requesting PEP at an academic hospital between 2001-2021 in Toronto, Canada. Patients were classified as HBV immune or susceptible based on laboratory tests. Among HBV-susceptible individuals, we quantified how often HBV vaccine and/or hepatitis B immune globulin (HBIG) were administered.ResultsWe identified 2018 PEP episodes, 75.3% being for sexual exposures. Mean age was 33.6 years. Among 1593 (78.9%) participants with available HBV testing data, six (0.4%) tested HBsAg-positive. Of 2018 episodes, 56.8% were among HBV-immune and 19.8% among HBV-susceptible participants; 23.1% were among participants of unknown status. Of susceptible participants, 55 (13.8%) received HBIG and 143 (35.8%) received HBV vaccinations.ConclusionsHBV prevalence was low but roughly one-fifth of patients seeking HIV PEP were HBV-susceptible. HBIG use was inconsistent with current guidelines, and a minority of HBV-susceptible individuals were vaccinated. More systematic HBV testing, increased HBV vaccination and more rational use of HBIG are needed in those seeking HIV PEP.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".