Ending TB in Southeast Asia: current resources are not enough
Bibliographic record
Abstract
The Southeast Asia Region continues to battle tuberculosis (TB) as one of its most severe health and development challenges. Unless there is a substantial increase in investments for TB prevention, diagnosis, care and treatment, there will be catastrophic effects for the region. The uncontrolled TB burden impacts socioeconomic development and increase of drug resistance in the region. Based on epidemiological inputs from a mathematical model, a costing analysis estimates that the desired targets of ending TB are achievable with additional interventions, and critical thresholds require an increase in spending by almost double the current levels. The data source for financial allocation to TB programmes is the report submitted by countries to WHO, while projections are based on modelling. The model accounts for funding needs for all strategies based on published data and accounts for programme and patient costs. This paper delineates the resource needs, availability and gaps of ending TB in the region. It is estimated that close to US$2 billion per year are needed in the region for TB-related activities for a meaningful bending of the incidence curve towards ending TB.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".