Direct comparison of three residual risk models for hepatitis B virus window period infections using updated input parameters
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Comparison of two models for estimating residual transfusion transmission risk by NAT screened window period (WP) donations in South African repeat donors gave identical results for HIV but not for HBV. In order to understand discrepant HBV modelling outcomes, the values of input parameters in three HBV WP risk models were reviewed and subsequently applied to the same South African screening data generated by HBsAg PRISM and two NAT assays (Ultrio and Ultrio Plus). Two of the models were also compared using individual donation (ID)-NAT screening data from different geographical regions. METHODS: Values of input parameters were derived from two published data sources and used in three risk models [(1) the incidence rate-WP risk day equivalent model, (2) the NAT yield WP ratio model and (3) the anti-HBc-negative HBsAg yield period ratio model] and subsequently applied to the same ID-NAT screening data. RESULTS: The HBV WP transmission risk in South African repeat donations during a one-year Ultrio Plus NAT screening period was estimated as 22, 43 and 17 per million, respectively, for the three models, as compared to 56, 117 and 48 per million for HBsAg PRISM screening. The approximate two-fold higher estimate calculated with the NAT yield WP ratio model was corroborated in repeat donations from three of four regions in a multi-regional study. When another set of model input values (with shorter viraemia periods and a higher proportion of acute occult infections) was applied to the South African screening data, the relative difference in risk estimates between the three models became smaller. CONCLUSIONS: Window period risk modelling for HBV is more complex than for HIV. Multiple factors affect the modelling outcomes. These include the values used for the length of transient HBsAg and HBV-DNA-positive phases, the proportion of acute occult and vaccine breakthrough infections and the assumption of random appearance of donors throughout the entire acute resolving infection phase. A substantial proportion of HBV WP NAT yields have very low viral load and lack donor follow-up data calling into question their definitive classification into the early acute (infectious) replication stage. Since these possible WP NAT yields most highly impact the NAT yield WP ratio model, we recommend relying on the more conservative estimates of the incidence rate-WP risk day equivalent model.
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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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".