Tuberculosis: evidence review for newly arriving immigrants and refugees
Bibliographic record
Abstract
BACKGROUND: The foreign-born population bears a disproportionate health burden from tuberculosis, with a rate of active tuberculosis 20 times that of the non-Aboriginal Canadian-born population, and could therefore benefit from tuberculosis screening programs. We reviewed evidence to determine the burden of tuberculosis in immigrant populations, to assess the effectiveness of screening and treatment programs for latent tuberculosis infection, and to identify potential interventions to improve effectiveness. METHODS: We performed a systematic search for evidence of the burden of tuberculosis in immigrant populations and the benefits and harms, applicability, clinical considerations, and implementation issues of screening and treatment programs for latent tuberculosis infection in the general and immigrant populations. The quality of this evidence was assessed and ranked using the GRADE approach (Grading of Recommendations Assessment, Development and Evaluation). RESULTS: Chemoprophylaxis with isoniazid is highly efficacious in decreasing the development of active tuberculosis in people with latent tuberculosis infection who adhere to treatment. Monitoring for hepatotoxicity is required at all ages, but close monitoring is required in those 50 years of age and older. Adherence to screening and treatment for latent tuberculosis infection is poor, but it can be increased if care is delivered in a culturally sensitive manner. INTERPRETATION: Immigrant populations have high rates of active tuberculosis that could be decreased by screening for and treating latent tuberculosis infection. Several patient, provider and infrastructure barriers, poor diagnostic tests, and the long treatment course, however, limit effectiveness of current programs. Novel approaches that educate and engage patients, their communities and primary care practitioners might improve the effectiveness of these programs.
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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 | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".