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Record W2930734343 · doi:10.1128/jcm.02057-18

Diagnostic Accuracy of Stool Xpert MTB/RIF for Detection of Pulmonary Tuberculosis in Children: a Systematic Review and Meta-analysis

2019· review· en· W2930734343 on OpenAlexafffund
Emily MacLean, Giorgia Sulis, Claudia M. Denkinger, James C. Johnston, Madhukar Pai, Faiz Ahmad Khan

Bibliographic record

VenueJournal of Clinical Microbiology · 2019
Typereview
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsMcGill University Health CentreBC Centre for Disease ControlUniversity of British ColumbiaMcGill University
FundersFonds de Recherche du Québec - Santé
KeywordsPulmonary tuberculosisMedicineTuberculosisNucleic Acid Amplification TestsMeta-analysisDiagnostic testDiagnostic accuracyMycobacterium tuberculosisVirologyInternal medicinePathologyPediatrics

Abstract

fetched live from OpenAlex

= 1,681) were included. Median participant ages ranged from 1.3 to 10.6 years. Protocols for stool processing and testing varied substantially, with differences in reagents and methods of homogenization and filtering. Against the microbiological reference standard, the pooled sensitivity and specificity of stool Xpert were 67% (95% confidence interval [CI], 52 to 79%) and 99% (95% CI, 98 to 99%), respectively. Sensitivity was higher among children with HIV (79% [95% CI, 68 to 87%] versus 60% [95% CI, 44 to 74%] among HIV-uninfected children). Heterogeneity was high. Data were insufficient for subgroup analyses among children under the age of 5 years, the most relevant target population. Stool Xpert could be a noninvasive method of ruling in PTB in children, particularly those with HIV. However, studies focused on children under 5 years of age are needed, and generalizability of the evidence is limited by the lack of standardized stool preparation and testing protocols.

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)0.010
metaresearch head score (Gemma)0.084
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Meta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.694
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.084
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0290.010
Bibliometrics0.0010.001
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 head0.171
GPT teacher head0.485
Teacher spread0.314 · 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 designMeta-analysis
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

Citations99
Published2019
Admission routes2
Has abstractyes

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