Hepatitis D double reflex testing of all hepatitis B carriers in low-HBV- and high-HBV/HDV-prevalence countries
Bibliographic record
Abstract
Hepatitis D virus (HDV) infection occurs as a coinfection with hepatitis B and increases the risk of hepatocellular carcinoma, decompensated cirrhosis, and mortality compared to hepatitis B virus (HBV) monoinfection. Reliable estimates of the prevalence of HDV infection and disease burden are essential to formulate strategies to find coinfected individuals more effectively and efficiently. The global prevalence of HBV infections was estimated to be 262,240,000 in 2021. Only 1,994,000 of the HBV infections were newly diagnosed in 2021, with more than half of the new diagnoses made in China. Our initial estimates indicated a much lower prevalence of HDV antibody (anti-HDV) and HDV RNA positivity than previously reported in published studies. Accurate estimates of HDV prevalence are needed. The most effective method to generate estimates of the prevalence of anti-HDV and HDV RNA positivity and to find undiagnosed individuals at the national level is to implement double reflex testing. This requires anti-HDV testing of all hepatitis B surface antigen-positive individuals and HDV RNA testing of all anti-HDV-positive individuals. This strategy is manageable for healthcare systems since the number of newly diagnosed HBV cases is low. At the global level, a comprehensive HDV screening strategy would require only 1,994,000 HDV antibody tests and less than 89,000 HDV PCR tests. Double reflex testing is the preferred strategy in countries with a low prevalence of HBV and those with a high prevalence of both HBV and HDV. For example, in the European Union and North America only 35,000 and 22,000 cases, respectively, will require anti-HDV testing annually.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".