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Record W2769432401 · doi:10.1111/tid.12825

Detection of human immunodeficiency virus, hepatitis C virus, and hepatitis B virus in postmortem blood specimens using infectious disease assays licensed for cadaveric donor screening

2017· article· en· W2769432401 on OpenAlexaff
Melissa A. Greenwald, Stephen Kerby, Kori Francis, Anna C. Noller, William T. Gormley, Robin Biswas, Richard A. Forshee

Bibliographic record

VenueTransplant Infectious Disease · 2017
Typearticle
Languageen
FieldMedicine
TopicHepatitis C virus research
Canadian institutionsOffice of the Chief Medical Examiner
Fundersnot available
KeywordsMedicineVirologyHepatitis B virusHepatitis C virusVirusNucleic acid testAntibodyHepatitis BDiseaseImmunologyInfectious disease (medical specialty)PathologyCoronavirus disease 2019 (COVID-19)

Abstract

fetched live from OpenAlex

BACKGROUND: Evaluation of assay performance on postmortem blood specimens (obtained after cessation of the heartbeat) presents unique scientific and regulatory challenges. In the United States, assay performance is evaluated in part by spiking postmortem specimens. METHODS: Fifty-four specimens obtained from decedents known to be infected with human immunodeficiency virus (HIV), hepatitis C virus (HCV), or hepatitis B virus (HBV), including some coinfections, were tested for each virus using Food and Drug Administration (FDA)-licensed donor screening tests for nucleic acid, antibody, and antigen. RESULTS: For each disease, >95% of subjects who were reported to have an infection at the time of death had a positive test result on at least one of the donor screening assays for that infection. CONCLUSION: Licensed donor screening tests were positive on postmortem specimens obtained within 24 hours of death from individuals dying with HIV, HCV, and/or HBV, and were able to detect presence of the virus. The use of multiple tests (including antibody and direct viral detection methods) is necessary to adequately evaluate donors.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.199
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.030
GPT teacher head0.306
Teacher spread0.276 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations12
Published2017
Admission routes1
Has abstractyes

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