Rapid Point-of-Care First-Line Screening Tests for Hepatitis B Infection: A Meta-Analysis of Diagnostic Accuracy (1980–2010)
Bibliographic record
Abstract
OBJECTIVES: Three-hundred fifty million people worldwide are chronically infected with Hepatitis B, with four million acute infections annually. With infection concentrated in hard-to-reach populations and low resource settings, rapid point-of-care (POC) tests offer an efficient screening alternative to laboratory tests. We conducted a meta-analysis to evaluate accuracy of rapid POC tests screening for Hepatitis B. METHODS: Two reviewers searched four databases, critiqued quality. A hierarchical Bayesian meta-analysis correcting for imperfect reference standards was used. Based on components of the antigen-antibody response, 17 studies were stratified into three subgroups: (i) Hepatitis B surface antigen (HBsAg) tests; (ii) anti-HBsAg tests, and (iii) HBs+eAg tests. Further, we pooled estimates on individual tests with sufficient data. RESULTS: In subgroup 1, the pooled sensitivity (Sn) was 94.76% (95% credible interval (CrI): 90.08-98.23%) and specificity (Sp) was 99.54% (95% CrI: 99.03-99.95%). The Determine test reported a pooled Sn 98.2% (95% CrI: 94.7, 99.9) and Sp 99.9% (95% CrI: 99.3, 100); in subgroup 2, Sn 93.2% (95% CrI: 85.1, 98.5), Sp 93.1% (95% CrI: 81.9, 99.9); and in subgroup 3, the Binax test showed Sn 95.5% (95% CrI: 88.9, 99.4), Sp 99.8% (95% CrI: 99.3, 100). CONCLUSIONS: HBsAg tests, including Determine, and the HBs+eAg test, Binax showed high accuracy. Improvements in sensitivity of antibody-based tests will enhance their potential for global first-line screening.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.011 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".