A Rapid Point-of-Care Test for the Serodiagnosis of Hepatitis Delta Virus Infection
Bibliographic record
Abstract
Hepatitis Delta virus (HDV) is a satellite of the Hepatitis B virus (HBV) and causes severe liver disease. The estimated prevalence of 15-20 million infected people worldwide may be underestimated as international diagnostic guidelines are not routinely followed. Possible reasons for this include the limited awareness among healthcare providers, the requirement for costly equipment and specialized training, and a lack of access to reliable tests in regions with poor medical infrastructure. In this study, we developed an HDV rapid test for the detection of antibodies against the hepatitis delta antigen (anti-HDV) in serum and plasma. The test is based on a novel recombinant large hepatitis delta antigen that can detect anti-HDV in a concentration-dependent manner with pan-genotypic activity across all known HDV genotypes. We evaluated the performance of this test on a cohort of 474 patient samples and found that it has a sensitivity of 94.6% (314/332) and a specificity of 100% (142/142) when compared to a diagnostic gold-standard ELISA. It also works robustly for a broad range of anti-HDV titers. We anticipate this novel HDV rapid test to be an important tool for epidemiological studies and clinical diagnostics, especially in regions that currently lack access to reliable HDV testing.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".