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Record W7030381475

A new antibody/antigen combination rapid test to detect acute HIV infection: A synthesis of evidence

2015· dissertation· en· W7030381475 on OpenAlexaff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2015
Typedissertation
Languageen
FieldEngineering
TopicMilitary Technology and Strategies
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsGold standard (test)Random effects modelBayesian probabilityHuman immunodeficiency virus (HIV)Meta-analysisSample size determination
DOInot available

Abstract

fetched live from OpenAlex

Background: Rapid and point-of-care diagnostics have expanded access to HIV testing, with fourth generation HIV rapid tests (Ag/Ab combo) now offering the potential of timely detection of acute HIV infection, when HIV is highly infectious. The aim of this thesis is to synthesize evidence on the global diagnostic performance of the only FDA-approved fourth generation rapid test, the Determine HIV 1/2 Ag/Ab Combo RT, to detect acute HIV in adults.Methods: We performed a systematic review and meta-analysis, searching Medline, Embase, PubMed, BIOSIS, The Cochrane Library, LILACS, and African Index Medicus, including conferences, bibliographies, and citations. Studies were included if they evaluated the Determine Combo Rapid Test in adults, against a reference standard. Two reviewers independently extracted data and assessed study quality with QUADAS-2. Our main outcomes of interest were sensitivity and specificity (overall, plus antigen and antibody components). Data from 17 studies (n=21599 patient samples) were pooled using a Bayesian hierarchical random effects meta-analysis model, which assumed a perfect gold standard was available for each study. To explore the extent to which the pooled estimates obtained through this model might differ under different assumptions, data were pooled using a hierarchical random effects model which assumed varying thresholds for positivity, allowing sensitivity and specificity to be correlated within each study. We analyzed subgroups by blood sample and study design for each model.Results: Using the Bayesian model which assumed a perfect gold standard, the overall pooled sensitivity for the device was 88.5%, 95% credible interval (CrI) [80.1 – 93.4], and overall pooled specificity was 99.1%, 95% CrI [97.3 – 99.8]. Pooled sensitivity of the antigen component was 12.3%, 95% CrI [1.1 – 44.2], with a pooled antigen specificity of 99.7%, 95% CrI [96.8 – 100]. Pooled sensitivity of the antibody component was 97.3%, 95% CrI [60.7 – 99.9], and pooled antibody specificity was 99.6%, 95% CrI [99.0 – 99.8]. Estimates using the continuous threshold model did not differ notably from the perfect gold standard model, giving us confidence that our estimates are robust. Individual study limitations included failure to blind reference standard results, and selecting patients or samples based on HIV status, resulting in potential for bias. None of the included studies were considered to have a low risk of bias. Data limitations prevented sub-group analyses by reference standards, and statistical exploration of the effect of patient case-mix on accuracy. Conclusions: HIV infection is accurately detected by the Determine HIV Combo in individuals who have seroconverted; however the diagnostic accuracy of the antigen component needs to be improved for detecting acute HIV infections at point-of-care.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.024
metaresearch head score (Gemma)0.081
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.024
Threshold uncertainty score0.124

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.081
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0110.018
Bibliometrics0.0090.006
Science and technology studies0.0010.001
Scholarly communication0.0050.003
Open science0.0030.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.025
GPT teacher head0.270
Teacher spread0.244 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
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

Citations0
Published2015
Admission routes1
Has abstractyes

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