Towards tuberculosis elimination: an action framework for low-incidence countries
Bibliographic record
Abstract
This paper describes an action framework for countries with low tuberculosis (TB) incidence (<100 TB cases per million population) that are striving for TB elimination. The framework sets out priority interventions required for these countries to progress first towards "pre-elimination" (<10 cases per million) and eventually the elimination of TB as a public health problem (less than one case per million). TB epidemiology in most low-incidence countries is characterised by a low rate of transmission in the general population, occasional outbreaks, a majority of TB cases generated from progression of latent TB infection (LTBI) rather than local transmission, concentration to certain vulnerable and hard-to-reach risk groups, and challenges posed by cross-border migration. Common health system challenges are that political commitment, funding, clinical expertise and general awareness of TB diminishes as TB incidence falls. The framework presents a tailored response to these challenges, grouped into eight priority action areas: 1) ensure political commitment, funding and stewardship for planning and essential services; 2) address the most vulnerable and hard-to-reach groups; 3) address special needs of migrants and cross-border issues; 4) undertake screening for active TB and LTBI in TB contacts and selected high-risk groups, and provide appropriate treatment; 5) optimise the prevention and care of drug-resistant TB; 6) ensure continued surveillance, programme monitoring and evaluation and case-based data management; 7) invest in research and new tools; and 8) support global TB prevention, care and control. The overall approach needs to be multisectorial, focusing on equitable access to high-quality diagnosis and care, and on addressing the social determinants of TB. Because of increasing globalisation and population mobility, the response needs to have both national and global dimensions.
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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 | 0.065 | 0.020 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.012 | 0.025 |
| Scholarly communication | 0.023 | 0.015 |
| Open science | 0.010 | 0.028 |
| Research integrity | 0.022 | 0.016 |
| 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".