Management of latent<i>Mycobacterium tuberculosis</i>infection: WHO guidelines for low tuberculosis burden countries
Bibliographic record
Abstract
Latent tuberculosis infection (LTBI) is characterised by the presence of immune responses to previously acquired Mycobacterium tuberculosis infection without clinical evidence of active tuberculosis (TB). Here we report evidence-based guidelines from the World Health Organization for a public health approach to the management of LTBI in high risk individuals in countries with high or middle upper income and TB incidence of <100 per 100 000 per year. The guidelines strongly recommend systematic testing and treatment of LTBI in people living with HIV, adult and child contacts of pulmonary TB cases, patients initiating anti-tumour necrosis factor treatment, patients receiving dialysis, patients preparing for organ or haematological transplantation, and patients with silicosis. In prisoners, healthcare workers, immigrants from high TB burden countries, homeless persons and illicit drug users, systematic testing and treatment of LTBI is conditionally recommended, according to TB epidemiology and resource availability. Either commercial interferon-gamma release assays or Mantoux tuberculin skin testing could be used to test for LTBI. Chest radiography should be performed before LTBI treatment to rule out active TB disease. Recommended treatment regimens for LTBI include: 6 or 9 month isoniazid; 12 week rifapentine plus isoniazid; 3-4 month isoniazid plus rifampicin; or 3-4 month rifampicin alone.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".