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Record W4308193957 · doi:10.1371/journal.pone.0277255

Optimizing tuberculosis screening for immigrants in southern New Brunswick: A pilot study protocol

2022· article· en· W4308193957 on OpenAlexafffundabout
Isdore Chola Shamputa, Duyên Thi Kim Nguyêñ, Doaa Higazy, Amani Abdelhadi, Hope Mackenzie, Michelle Reddin, Kimberley Barker, Duncan Webster

Bibliographic record

VenuePLoS ONE · 2022
Typearticle
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsDalhousie UniversitySaint John Regional HospitalGovernment of New BrunswickMinistry of HealthUniversity of New Brunswick
FundersNew Brunswick Innovation FoundationFondation de la recherche en santé du Nouveau-Brunswick
KeywordsLatent tuberculosisMedicineTuberculosisImmigrationSnowball samplingFamily medicineIncidence (geometry)Interferon gamma release assayEnvironmental healthMycobacterium tuberculosisPathology

Abstract

fetched live from OpenAlex

INTRODUCTION: Immigrants from high tuberculosis-burdened countries have been shown to have an increased risk of latent tuberculosis infection (LTBI). To reduce the risk of increased tuberculosis cases in Canada, the country has a comprehensive immigration medical examination process that identifies individuals with active tuberculosis using chest X-ray; however, it fails to identify LTBI. The lack of LTBI identification is concerning because immigrants with LTBI are at an increased risk of developing active tuberculosis within their first few years of migration due to stressful experiences common to many immigrants. OBJECTIVES: The goal of this pilot study is to improve the current LTBI screening protocols among immigrants from high tuberculosis incidence countries and to better prevent and manage tuberculosis cases, by introducing an LTBI screening pilot program. The objectives are threefold: 1) to screen LTBI in immigrants from high tuberculosis incidence countries, including immigrants identified as being at risk of LTBI by the NB health care system, using the QuantiFERON-TB Gold Plus interferon-gamma release assay (IGRA); 2) to offer LTBI treatment and supports to those identified as having LTBI; and 3) to assess immigrant and health care providers (HCPs) satisfaction of the LTBI screening pilot program. METHODS: This cross-sectional study seeks to recruit 288 participants. Participants will be recruited via posters, social media platforms, invitations at immigrant wellness check-ups, presentations to local ethnocultural groups, and by snowball sampling. Consenting participants will be asked to submit a blood sample for LTBI screening; if positive, participants will be assessed and offered treatment for LTBI based on clinical assessment. Participants and HCPs' feedback will be gathered via short questionnaires. For the quantitative portion of the study, descriptive statistics will be used to summarize participant characteristics and feedback. Simultaneous logistic regression will be performed to identify variables associated with the IGRA test outcome and evidence of increased CD8 T-cell immune response among those found to be LTBI-positive. Qualitative results will be analyzed using inductive thematic analysis. DISCUSSION: The findings from this study will allow us to understand the role of the IGRA LTBI screening assay and its feasibility and acceptability by immigrants and HCPs in New Brunswick. The findings will additionally provide information on the enhancers and barriers of LTBI screening and management useful in determining how best to expand the LTBI screening program if deemed appropriate.

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)0.021
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.962
Threshold uncertainty score0.112

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.010
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.002
Science and technology studies0.0050.002
Scholarly communication0.0020.002
Open science0.0040.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0290.005

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 head0.140
GPT teacher head0.356
Teacher spread0.215 · 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 designNot applicable
Domainnot available
GenreProtocol

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

Citations3
Published2022
Admission routes3
Has abstractyes

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