Laboratory development of an RNA quantitative RT-PCR assay reporting in international units for hepatitis D virus
Bibliographic record
Abstract
Introduction: Chronic hepatitis D virus (HDV) is associated with rapid progression to severe liver disease. Co-infection with HDV and hepatitis B virus is likely underdiagnosed due to challenges in diagnostic test availability and standardization. With new HDV antiviral options, HDV RNA quantification is essential for understanding the patient response to treatment. To this end, a quantitative real-time reverse transcription PCR (qRT-PCR) assay utilizing synthetic RNA calibrators and a conversion factor to quantify HDV RNA in WHO international standard units (IU/mL) was developed and validated. Methods: 1 copies/μL) were calibrated against the WHO standard and a conversion factor calculated to convert copies/μL to IU/mL. Assay validation and evaluation was conducted, including use of specimens from 8 HDV genotypes and comparison to a commercial assay. Results: The assay lower limit of detection was determined by probit analysis to be 11 IU/mL (8.63-15.78 95% CI). Inter- and intra-assay coefficient of variation analysis showed 96.6% precision and 90.6% accuracy. A conversion factor of 16.5 was used to convert copies/μL to IU/mL. All 8 HDV genotypes were quantified by the assay and commercial assay comparison showed good agreement. Discussion: The developed assay has clinical utility for the sensitive and specific quantitative monitoring of HDV RNA, appropriate for medium to high throughput laboratories.
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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 | 0.016 | 0.017 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.007 |
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".