Tuberculosis diagnostics: which target product profiles should be prioritised?
Bibliographic record
Abstract
To the Editor: Globally, a third of all tuberculosis (TB) cases are not notified and many patients do not receive drug susceptibility testing (DST) [1]. New diagnostics can contribute to increased case detection, shorter diagnostic delay and reduced TB transmission. While the Xpert MTB/RIF assay (Cepheid Inc., Sunnyvale, CA, USA) is a much needed breakthrough [2], it may not reach lower tiers of the healthcare system [3] and doesn’t meet all needs ( e.g. cannot detect resistance against multiple drugs). Several promising diagnostics are under development and companies are showing interest in TB products, inspired by the success of Xpert MTB/RIF [4]. But which new TB diagnostics should they invest in, and what is the potential market size for these products? Stakeholders have expressed a need for different products, including a test for childhood TB [5], a simple point-of-care-test for active pulmonary TB [6], a molecular smear replacement test [7], DST for expected new drug regimens [8], predictive biomarkers for latent TB infection (LTBI) [9], and treatment monitoring [10]. Given the variety of these needs, it is important for product developers to have access to: 1) a clearly identified list of diagnostics that are considered high priority by the TB community; 2) well developed, detailed target product profiles (TPPs) for priority diagnostics; and 3) up-to-date market size estimates for the priority TPPs. These issues are being addressed by ongoing activities, supported by the Bill and Melinda Gates Foundation (Seattle, WA, USA) and other partners [3, 7, 11]. TPPs are useful to align the end-users needs …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.013 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".