MétaCan
Menu
Back to cohort
Record W2345921197 · doi:10.1371/journal.pone.0154825

Latent Tuberculosis in Pregnancy: A Systematic Review

2016· review· en· W2345921197 on OpenAlexaff
Isabelle Malhamé, Maxime Cormier, Jordan Sugarman, Kevin Schwartzman

Bibliographic record

VenuePLoS ONE · 2016
Typereview
Languageen
FieldMedicine
TopicDiagnosis and treatment of tuberculosis
Canadian institutionsMcGill UniversityMontreal Heart Institute
Fundersnot available
KeywordsTuberculosisExtensively drug-resistant tuberculosisPregnancyLatent tuberculosisMedicineMEDLINEObstetricsMycobacterium tuberculosisBioinformaticsBiologyPathologyGenetics

Abstract

fetched live from OpenAlex

BACKGROUND: In countries with low tuberculosis (TB) incidence, immigrants from higher incidence countries represent the major pool of individuals with latent TB infection (LTBI). The antenatal period represents an opportunity for immigrant women to access the medical system, and hence for potential screening and treatment of LTBI. However, such screening and treatment during pregnancy remains controversial. OBJECTIVES: In order to further understand the prevalence, natural history, screening and management of LTBI in pregnancy, we conducted a systematic literature review addressing the screening and treatment of LTBI, in pregnant women without known HIV infection. METHODS: A systematic review of 4 databases (Embase, Embase Classic, Medline, Cochrane Library) covering articles published from January 1st 1980 to April 30th 2014. Articles in English, French or Spanish with relevant information on prevalence, natural history, screening tools, screening strategies and treatment of LTBI during pregnancy were eligible for inclusion. Articles were excluded if (1) Full text was not available (2) they were case series or case studies (3) they focused exclusively on prevalence, diagnosis and treatment of active TB (4) the study population was exclusively HIV-infected. RESULTS: Of 4,193 titles initially identified, 208 abstracts were eligible for review. Of these, 30 articles qualified for full text review and 22 were retained: 3 cohort studies, 2 case-control studies, and 17 cross-sectional studies. In the USA, the estimated prevalence of LTBI ranged from 14 to 48% in women tested, and tuberculin skin test (TST) positivity was associated with ethnicity. One study suggested that incidence of active TB was significantly increased during the 180 days postpartum (Incidence rate ratio, 1.95 (95% CI 1.24-3.07). There was a high level of adherence with both skin testing (between 90-100%) and chest radiography (93-100%.). In three studies from low incidence settings, concordance between TST and an interferon-gamma release assay was 77, 88 and 91% with kappa values ranging from 0.26 to 0.45. In low incidence settings, an IGRA may be more specific and less sensitive than TST, and results do not appear to be altered by pregnancy. The proportion of women who attended follow-up visits after positive tuberculin tests varied from 14 to 69%, while 5 to 42% of those who attended follow-up visits completed a minimum of 6 months of isoniazid treatment. One study raised the possibility of an association of pregnancy/post-partum state with INH hepatitis (risk ratio 2,5, 95% CI 0.8-8.2) and fatal hepatotoxicity (rate ratio 4.0, 95% CI 0.2-258). One study deemed INH safe during breastfeeding based on peak concentrations in plasma and breast milk after INH administration. CONCLUSION: Pregnancy is an opportunity to screen for LTBI. Interferon-gamma release assays are likely comparable to tuberculin skin tests and may be used during pregnancy. Efforts should be made to improve adherence with follow-up and treatment post-partum. Further data are needed with respect to safety and feasibility of antepartum INH therapy, and with respect to alternative treatment regimens.

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)0.005
metaresearch head score (Gemma)0.028
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: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.028
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0080.006
Bibliometrics0.0090.011
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.098
GPT teacher head0.317
Teacher spread0.219 · 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
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

Citations73
Published2016
Admission routes1
Has abstractyes

Explore more

Same venuePLoS ONESame topicDiagnosis and treatment of tuberculosisFrench-language works237,207