Policies, practices, opportunities and challenges for tuberculosis screening: a global survey of national tuberculosis programmes
Bibliographic record
Abstract
INTRODUCTION: There are limited published data on how countries carry out screening for tuberculosis (TB) disease and what the perceived challenges are for implementing screening from a country perspective. Understanding these factors are important to enable better planning and support for the roll-out of appropriate screening interventions. METHODS: We conducted a cross-sectional survey of national TB programmes from countries reporting >1000 TB cases annually. RESULTS: Sixty of 123 countries responded, representing 82% of the global TB burden. Only 35% of countries had a policy to screen for TB in all four key risk groups identified by WHO, 66% carried out all six WHO-recommended steps to implement screening and 39% collected all seven of the WHO-recommended data points for monitoring activity. Although 68% of countries planned to increase CXR-based screening, 90% reported at least one significant barrier to implementing this, and 20% were not aware of computer-aided detection (CAD) software technology. CONCLUSION: Although chest X-ray and CAD use are expanding and hold promise as tools to find people with TB, many programmes do not have adequate access to them. While global policy is in place that recommends the use of these tools, efforts should be made to support countries tackling these barriers.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".