Tuberculosis Diagnostics: State of the Art and Future Directions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it