Scaling up evidence-based approaches to tuberculosis screening in prisons
Bibliographic record
Abstract
People deprived of liberty have among the highest rates of tuberculosis globally. The incidence of tuberculosis is ten times greater than the incidence of tuberculosis in the general population. In 2021, WHO updated its guidance to strongly recommend systematic screening for tuberculosis in prisons and penitentiary systems. Which case-finding strategies should be adopted, and how to effectively implement these strategies in these settings, will be crucial questions facing ministries of health and justice. In this Viewpoint, we review the evidence base for tuberculosis screening and diagnostic strategies in prisons, highlighting promising approaches and knowledge gaps. Drawing upon past experiences of implementing active case-finding and care programmes in settings with a high tuberculosis burden, we discuss challenges and opportunities for improving the tuberculosis diagnosis and treatment cascade in these settings. We argue that improved transparency in reporting of tuberculosis notifications and outcomes in prisons and renewed focus and resourcing from WHO and other stakeholders will be crucial for building the commitment and investments needed from countries to address the continued crisis of tuberculosis in prisons.
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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 | 0.023 | 0.080 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".