Evidence map›Paper›PMID 37632077›Full record

ArticleViruses2023

Clinical Validity of a Machine Learning Decision Support System for Early Detection of Hepatitis B Virus: A Binational External Validation Study.

Busayo I Ajuwon, Alice Richardson, Katrina Roper, Brett A Lidbury

Abstract read
In one paragraph

Article in Viruses, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Busayo I AjuwonNational Centre for Epidemiology and Population Health, ANU College of Health and Medicine, The Australian National University, Acton, Canberra, ACT 2601, Australia.ORCID 0000-0003-4034-3617
Alice RichardsonStatistical Support Network, The Australian National University, Acton, Canberra, ACT 2601, Australia.ORCID 0000-0001-7084-1524
Katrina RoperNational Centre for Epidemiology and Population Health, ANU College of Health and Medicine, The Australian National University, Acton, Canberra, ACT 2601, Australia.
Brett A LidburyNational Centre for Epidemiology and Population Health, ANU College of Health and Medicine, The Australian National University, Acton, Canberra, ACT 2601, Australia.ORCID 0000-0003-2211-989X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

HepB LiveTest is a machine learning decision support system developed for the early detection of hepatitis B virus (HBV). However, there is a lack of evidence on its generalisability. In this study, we aimed to externally assess the clinical validity and portability of HepB LiveTest in predicting HBV infection among independent patient cohorts from Nigeria and Australia. The performance of HepB LiveTest was evaluated by constructing receiver operating characteristic curves and estimating the area under the curve. Delong's method was used to estimate the 95% confidence interval (CI) of the area under the receiver-operating characteristic curve (AUROC). Compared to the Australian cohort, patients in the derivation cohort of HepB LiveTest and the hospital-based Nigerian cohort were younger (mean age, 45.5 years vs. 38.8 years vs. 40.8 years, respectively;

Indexed as

Hepatitis BHepatitis B virusAgedAustraliaHumansMachine LearningMiddle AgedPhenylbutyratesgamma-hydroxy-gamma-ethyl-gamma-phenylbutyramidePhenylbutyratesclinical validityexternal validationhepatitis B virusHepB LiveTestmachine learning decision support system

Identifiers

PMID37632077
PMCPMC10458613

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.