TB preventive treatment in high- and intermediate-incidence countries: research needs for scale-up
Bibliographic record
Abstract
BACKGROUND: In 2018, the WHO Member States committed to providing TB preventive treatment (TPT) to at least 30 million people by 2022. However, only 6.3 million people had initiated TPT by the end of 2019. Major knowledge gaps and research needs in diagnosis, treatment and the programmatic management of TPT (PMTPT) require to be addressed urgently. METHODS: In September 2019, a group of stakeholders involved in PMTPT in high TB burden countries met to develop an action agenda to support the global expansion of PMTPT. RESULTS: Barriers at the health system level, and priorities for research to overcome these, were identified for each step of the PMTPT cascade. The need for data on TPT financing, gaps and coverage under national health insurance schemes, as well as the need for mathematical and cost-effectiveness modelling of the impact of TPT on TB incidence and mortality were highlighted. Specific research needs were identified for high-risk populations such as household contacts of any age and people living with HIV, as well as other people at risk. CONCLUSIONS: The meeting facilitated agreement on a set of actions needed to ensure that PMTPT continues to expand to achieve the End TB Strategy targets.
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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 | 0.089 | 0.130 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.023 | 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".