Tuberculosis Diagnostics in 2015: Landscape, Priorities, Needs, and Prospects
Bibliographic record
Abstract
In 2015, tuberculosis remains a major global health problem, and drug-resistant tuberculosis is a growing threat. Although tuberculosis diagnosis in many countries is still reliant on older tools, new diagnostics are changing the landscape. Stimulated, in part, by the success and roll out of Xpert MTB/RIF, there is now considerable interest in new technologies. The landscape looks promising, with a robust pipeline of new tools, particularly molecular diagnostics, and well over 50 companies actively engaged in product development. However, new diagnostics are yet to reach scale, and there needs to be greater convergence between diagnostics development and development of shorter-duration tuberculosis drug regimens. Another concern is the relative absence of non-sputum-based diagnostics in the pipeline for children and of biomarker tests for triage, cure, and progression of latent Mycobacterium tuberculosis infection. Several initiatives, described in this supplement, have been launched to further stimulate product development and policy, including assessment of needs and priorities, development of target product profiles, compilation of data on resistance-associated mutations, and assessment of market size and potential for new diagnostics. Advocacy is needed to increase funding for tuberculosis research and development, and governments in high-burden countries must invest more in tuberculosis control to meet post-2015 targets for care, control, and prevention.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".