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Record W2897940548 · doi:10.1097/coh.0000000000000512

Diagnosing active tuberculosis in people living with HIV

2018· review· en· W2897940548 on OpenAlexaff
Emily MacLean, Kavitha Saravu, Madhukar Pai

Bibliographic record

VenueCurrent Opinion in HIV and AIDS · 2018
Typereview
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsMcGill University
Fundersnot available
KeywordsLipoarabinomannanMedicineTuberculosisHuman immunodeficiency virus (HIV)Intensive care medicineMycobacterium tuberculosisDrug resistant tuberculosisSubclinical infectionDiagnostic testDrug resistanceImmunologyInternal medicinePediatricsPathologyBiology

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Diagnosis of tuberculosis (TB) in people living with HIV (PLHIV) remains a challenge, despite it being the most common comorbidity in this group. In this review, we provide an overview of tests for active TB, and their diagnostic performance in PLHIV. RECENT FINDINGS: New and updated diagnostic tests have better performance than traditional bacterial culture or smear microscopy in PLHIV. Recent developments in molecular tests have improved the sensitivity at which TB and drug susceptibility can be detected in PLHIV. Notably, the updated Xpert Ultra test can detect HIV-associated TB with high sensitivity, and a rapid lateral flow lipoarabinomannan-based assay has been shown to reduce TB-related mortality in hospitalized PLHIV. New directions in drug-susceptibility testing are being pursued, such as next-generation sequencing and line probe assays, but more evaluation in PLHIV is needed. There is growing understanding of subclinical TB, but methods to detect this type of TB are inadequate. SUMMARY: As diagnosis is the weakest link in the TB care cascade, newer more accurate TB tests must be scaled up and fully integrated into existing healthcare systems. Drug-susceptibility testing must become universal to ensure appropriate treatment regimens are prescribed, allowing TB clearance and inhibiting development of antimicrobial resistance.

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)0.001
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 score0.841
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.089
GPT teacher head0.414
Teacher spread0.325 · 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

Citations16
Published2018
Admission routes1
Has abstractyes

Explore more

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