A scoping review on the risk of tuberculosis in specific population groups: can we expand the World Health Organization recommendations?
Bibliographic record
Abstract
Since 2015, the World Health Organization (WHO) has recommended prioritising testing and treatment of tuberculosis (TB) infection (TBI) in 11 high-risk groups. With new options emerging for TB preventive treatment, we conducted a scoping review, in consultation with the WHO's Global Tuberculosis Programme, to explore the evidence for other population groups at potentially high risk of progression to active TB. We searched six databases for preprints and articles published between 2000 and August 2022. 18 out of 33 668 screened records were included (six meta-analyses and 12 original research studies). Most were observational studies reporting the incidence of active TB in a risk groupversuscontrol. Glomerular diseases had the strongest association with active TB (standardised incidence ratio 23.36, 95% CI 16.76–31.68) based on an unpublished study. Other conditions associated with increased risk of active TB included hepatitis C, malignancies, diabetes mellitus, rheumatoid arthritis and vitamin D deficiency. Corticosteroid use was also associated with increased risk in several studies, although heterogeneous definitions of exposure and indications for use challenge interpretation. Despite methodological limitations of the identified studies, expanding the recommendations for TBI screening and treatment to new risk groups such as those reported here should be considered. Further group-specific systematic reviews may provide additional data for decision-making.
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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 | 0.061 | 0.189 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.012 |
| Bibliometrics | 0.024 | 0.017 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".