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Record W2922423860 · doi:10.1093/cid/ciz188

Estimated Impact of World Health Organization Latent Tuberculosis Screening Guidelines in a Region With a Low Tuberculosis Incidence: Retrospective Cohort Study

2019· article· en· W2922423860 on OpenAlexafffundabout
Lisa A. Ronald, Jonathon R. Campbell, Caren Rose, Robert Balshaw, Kamila Romanowski, David Roth, Fawziah Marra, Kevin Schwartzman, Victoria Cook, James C. Johnston

Bibliographic record

VenueClinical Infectious Diseases · 2019
Typearticle
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsUniversity of ManitobaMcGill UniversityGeorge & Fay Yee Centre for Healthcare InnovationUniversity of British ColumbiaBC Centre for Disease Control
FundersCanadian Institutes of Health Research
KeywordsMedicineTuberculosisIncidence (geometry)Retrospective cohort studyCohortLatent tuberculosisCohort studyEnvironmental healthFamily medicineMycobacterium tuberculosisInternal medicinePathology

Abstract

fetched live from OpenAlex

BACKGROUND: Latent tuberculosis infection (LTBI) screening and treatment is a key component of the World Health Organization (WHO) EndTB Strategy, but the impact of LTBI screening and treatment at a population level is unclear. We aimed to estimate the impact of LTBI screening and treatment in a population of migrants to British Columbia (BC), Canada. METHODS: This retrospective cohort included all individuals (N = 1 080 908) who immigrated to Canada as permanent residents between 1985 and 2012 and were residents in BC at any time up to 2013. Multiple administrative databases were linked to identify people with risk factors who met the WHO strong recommendations for screening: people with tuberculosis (TB) contact, with human immunodeficiency virus, on dialysis, with tumor necrosis factor-alpha inhibitors, who had an organ/haematological transplant, or with silicosis. Additional TB risk factors included immunosuppressive medications, cancer, diabetes, and migration from a country with a high TB burden. We defined active TB as preventable if diagnosed ≥6 months after a risk factor diagnosis. We estimated the number of preventable TB cases, given optimal LTBI screening and treatment, based on these risk factors. RESULTS: There were 16 085 people (1.5%) identified with WHO strong risk factors. Of the 2814 people with active TB, 118 (4.2%) were considered preventable through screening with WHO risk factors. Less than half (49.4%) were considered preventable with expanded screening to include people migrating from countries with high TB burdens, people who had been prescribed immunosuppressive medications, or people with diabetes or cancer. CONCLUSIONS: The application of WHO LTBI strong recommendations for screening would have minimally impacted the TB incidence in this population. Further high-risk groups must be identified to develop an effective LTBI screening and treatment strategy for low-incidence regions.

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)0.003
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.446
Threshold uncertainty score0.888

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.054
GPT teacher head0.436
Teacher spread0.382 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations33
Published2019
Admission routes3
Has abstractyes

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