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Record W3011481800 · doi:10.1136/bmj.m549

Absolute risk of tuberculosis among untreated populations with a positive tuberculin skin test or interferon-gamma release assay result: systematic review and meta-analysis

2020· review· en· W3011481800 on OpenAlexafffund
Jonathon R. Campbell, Nicholas Winters, Dick Menzies

Bibliographic record

VenueBMJ · 2020
Typereview
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsMcGill UniversityMcGill University Health Centre
FundersCanadian Institutes of Health Research
KeywordsTuberculinTuberculosisMeta-analysisMedicineSkin testInterferon gammaImmunologyTuberculin testInterferon gamma release assayInternal medicineMycobacterium tuberculosisPathologyLatent tuberculosisCytokine

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine the annual rate of tuberculosis development after a positive tuberculin skin test (TST) or interferon-gamma release assay result (IGRA), or both, among untreated populations with characteristics believed to increase the risk of tuberculosis (at risk populations). DESIGN: Systematic review and meta-analysis. DATA SOURCES: Embase, Medline, and Cochrane Controlled Register of Trials from 1 January 1990 to 17 May 2019, for studies in humans published in English or French. Reference lists were reviewed. ELIGIBILITY CRITERIA AND DATA ANALYSIS: Retrospective or prospective cohorts and randomised trials that included at least 10 untreated participants who tested positive to tuberculosis antigens (contained in TST or IGRA, or both) followed for at least 12 months. Following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) and meta-analyses of observational studies in epidemiology (MOOSE) guidelines, two reviewers independently extracted study data and assessed quality using a modified quality assessment of diagnostic accuracy studies (QUADAS-2) tool. Data were pooled using random effects generalised linear mixed models. MAIN OUTCOME MEASURES: The primary outcome was tuberculosis incidence per 1000 person years among untreated participants who tested positive (TST or IGRA, or both) in different at risk subgroups. Secondary outcomes were the cumulative incidence of tuberculosis and incidence rate ratios among participants with a positive test result for latent tuberculosis infection compared with those with a negative test result in at risk subgroups. RESULTS: 122 of 5166 identified studies were included. In three general population studies, the incidence of tuberculosis among 33 811 participants with a TST induration of ≥10 mm was 0.3 (95% confidence interval 0.1 to 1.1) per 1000 person years. Among 116 197 positive test results for latent tuberculosis infection in 19 different at risk populations, incidence rates were consistently higher than those in the general population. Among all types of tuberculosis contacts, the incidence of tuberculosis was 17.0 (95% confidence interval 12.9 to 22.4) per 1000 person years for participants with a positive IGRA result and 8.4 (5.6 to 12.6) per 1000 person years for participants with a positive TST result of ≥5 mm. Among people living with HIV, the incidence of tuberculosis was 16.9 (10.5 to 27.3) for participants with a positive IGRA result and 27.1 (15.0 to 49.0) for participants with a positive TST result of ≥5 mm. Rates were also high for immigrants, people with silicosis or requiring dialysis, transplant recipients, and prisoners. Incidence rate ratios among test positive versus test negative participants were significantly greater than 1.0 in almost all risk groups, for all tests. CONCLUSIONS: The incidence of tuberculosis is substantial in numerous at risk populations after a positive TST or IGRA result. The information from this review should help inform clinical decisions to test and treat for latent tuberculosis infection. STUDY REGISTRATION: PROSPERO CRD42019136608.

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)0.003
metaresearch head score (Gemma)0.019
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 score0.545
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.019
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0180.004
Bibliometrics0.0010.003
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 head0.097
GPT teacher head0.398
Teacher spread0.302 · 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

Citations110
Published2020
Admission routes2
Has abstractyes

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