Tuberculosis Diagnostics: State of the Art and Future Directions
Bibliographic record
Abstract
Rapid and accurate diagnosis is critical for timely initiation of anti-tuberculosis (TB) treatment, but many people with TB (or TB symptoms) do not have access to adequate initial diagnosis. In many countries, TB diagnosis is still reliant on sputum microscopy, a test with known limitations. However, new diagnostics are starting to change the landscape. Stimulated, in part, by the success and rollout of Xpert MTB/RIF, an automated, molecular test, there is now considerable interest in new technologies. The landscape looks promising with a pipeline of new tools, particularly molecular diagnostics, and well over 50 companies actively engaged in product development, and many tests have been reviewed by WHO for policy endorsement. However, new diagnostics are yet to reach scale, and there needs to be greater convergence between diagnostics development and the development of shorter TB 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 latent TB progression. Increased investments are necessary to support biomarker discovery, validation, and translation into clinical tools. While transformative tools are being developed, high-burden countries will need to improve the efficiency of their health care delivery systems, ensure better uptake of new technologies, and achieve greater linkages across the TB and HIV care continuum. While we wait for next-generation technologies, national TB programs must scale up the best diagnostics currently available, and use implementation science to get the maximum impact.
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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 | 0.019 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.024 | 0.010 |
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".