Replacing smear microscopy for the diagnosis of tuberculosis: what is the market potential?
Bibliographic record
Abstract
To the Editor: Sputum smear microscopy (SSM) has been the cornerstone of tuberculosis (TB) diagnosis, and is mainly performed in peripheral microscopy centres attached to primary health centres where TB therapy can be administered. Although SSM is inexpensive and easy to perform with a limited infrastructure, the shortcomings are its relatively low sensitivity and its inability to detect drug-resistance. Thus, there is a need for a more sensitive technology that can replace microscopy [1, 2]. Several next-generation molecular diagnostics are under development with the specific intention of use in microscopy centres [3–5]. In a recent survey of 22 high-burden countries (HBCs), we showed that the conditions, equipment and expertise present in microscopy centres are challenging and need to be considered by product developers [6]. While the Xpert MTB/RIF (Cepheid Inc., Sunnyvale, CA, USA) assay is accurate, endorsed by the World Health Organization and is being implemented in many countries, it was not designed for use in peripheral microscopy centres [7, 8]. To assist product developers working on tests for use in microscopy centres, we have outlined the desirable test characteristics [1]. For companies to take on the challenge and invest in new diagnostics, an understanding of the potential market size is paramount [9]. A 2006 global TB diagnostics market analysis predicted that a smear replacement test applied at the level of peripheral clinics and microscopy centres would have a potential market size of 36.6 million tests per year in the 22 HBCs in 2020, assuming it would diagnose pulmonary, extrapulmonary and paediatric TB [10 …
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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 | 0.007 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.015 | 0.015 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".