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Record W3000937628 · doi:10.1111/vox.12889

Direct comparison of three residual risk models for hepatitis B virus window period infections using updated input parameters

2020· review· en· W3000937628 on OpenAlexaff
Nico Lelie, Marion Vermeulen, Harry van Drimmelen, Charl Coleman, Roberta Bruhn, Ravi Shankar Reddy, Michael P. Busch, Steve Kleinman

Bibliographic record

VenueVox Sanguinis · 2020
Typereview
Languageen
FieldMedicine
TopicHepatitis B Virus Studies
Canadian institutionsUniversity of VictoriaUniversity of British Columbia
Fundersnot available
KeywordsWindow periodHBsAgHepatitis B virusMedicineNatTransmission (telecommunications)Residual riskDemographyIncidence (geometry)Hepatitis BVirologyStatisticsInternal medicineImmunologyMathematicsVirusAntibodySerologyComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.952
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.138
GPT teacher head0.383
Teacher spread0.245 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations13
Published2020
Admission routes1
Has abstractyes

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