Potential Market for Novel Tuberculosis Diagnostics: Worth the Investment?
Bibliographic record
Abstract
BACKGROUND: The potential available market (PAM) for new diagnostics for tuberculosis that meet the specifications of the high-priority target product profiles (TPPs) is currently unknown. METHODS: We estimated the PAM in 2020 in 4 high-burden countries (South Africa, Brazil, China, and India) for tests that meet the specifications outlined in the TPPs. The yearly PAM was estimated for the most likely application of each TPP. RESULTS: In 2020 the PAM for all 4 countries together was estimated to be (1) 12M tests/year with a value of 48M-71M USD for a sputum smear-replacement test; (2) 16M tests/year with a value of 65M-97M USD for a biomarker test; (3) 18M tests/year with a value of 18M-35M USD for a triage test; (4) 12M tests/year with a value of 59M-2238M USD for a tuberculosis detection plus drug susceptibility test (DST) all-in-one or 1.5M tests/year for a DST that follows a positive tuberculosis detection test with a corresponding value of 75M-121M for both tuberculosis detection and DST. CONCLUSIONS: Although there is a considerable potential market for novel tuberculosis diagnostics that fit the specification of the TPPs in the 4 high-burden countries, the actual market for an individual product remains uncertain.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".