Challenges for Contact Tracing and Tuberculosis Preventive Therapy Scale-up
Bibliographic record
Abstract
Tuberculosis preventive therapyTherapyPreventive (TPT) is a key strategy to eliminate tuberculosis (TB) by 2050. However, less than one-fifth of those needing TPT complete it because of the many losses through the complex tuberculosis infection (TBI) cascade of care. The largest and higher-risk populationsPopulation targeted for TPT are people living with HIV and contactsContact of patients with pulmonary TB. New tests to detect TBI and shorter and better-tolerated treatment regimensRegimentreatment to treat TBI have been incorporated by several countries in recent years, but the public healthPublic health impact of these advances is poor, as many other barriers are still pending. This chapter reviews the progress and bottlenecks for scaling up TPT to contactsContact worldwide. The perspective for new tests, treatments, and innovative approaches is also discussed.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.036 | 0.022 |
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".