Screening for tuberculosis in migrants and visitors from high-incidence settings: present and future perspectives
Bibliographic record
Abstract
In most settings with a low incidence of tuberculosis (TB), foreign-born people make up the majority of TB cases, but the distribution of the TB risk among different migrant populations is often poorly quantified. In addition, screening practices for TB disease and latent TB infection (LTBI) vary widely. Addressing the risk of TB in international migrants is an essential component of TB prevention and care efforts in low-incidence countries, and strategies to systematically screen for, diagnose, treat and prevent TB among this group contribute to national and global TB elimination goals. This review provides an overview and critical assessment of TB screening practices that are focused on migrants and visitors from high to low TB incidence countries, including pre-migration screening and post-migration follow-up of those deemed to be at an increased risk of developing TB. We focus mainly on migrants who enter the destination countryviaapplication for a long-stay visa, as well as asylum seekers and refugees, but briefly consider issues related to short-term visitors and those with long-duration multiple-entry visas. Issues related to the screening of children and screening for LTBI are also explored.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".