Optimizing the cascade of prevention to protect people from tuberculosis: A potential game changer for reducing global tuberculosis incidence
Bibliographic record
Abstract
The provision of tuberculosis preventive treatment is one of the critical interventions to reduce tuberculosis incidence and ultimately eliminate the disease, yet we still miss appropriate tools for an impactful intervention and treatment coverage remains low. We used recent data, epidemiological estimates, and research findings to analyze the challenges of each step of the cascade of tuberculosis prevention that currently delay the strategy implementation. We addressed research gaps and implementation bottlenecks that withhold key actions in tuberculosis case finding, testing for tuberculosis infection, provision of preventive treatment with safer, shorter regimens and supporting people to complete their treatment. Empowering communities to generate demand for preventive therapy and other prevention services in a holistic manner and providing adequate financial support to sustain implementation are essential requirements. The adoption of an effective, universal monitoring and evaluation system is a prerequisite to provide general and granular insight, and to steer progress of the tuberculosis infection strategy at global and local level.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".