Advances in Molecular Diagnosis of Tuberculosis
Bibliographic record
Abstract
Molecular tests for tuberculosis (TB) have the potential to help reach the three million people with TB who are undiagnosed or not reported each year and to improve the quality of care TB patients receive by providing accurate, quick results, including rapid drug-susceptibility testing. The World Health Organization (WHO) has recommended the use of molecular nucleic acid amplification tests (NAATs) tests for TB detection instead of smear microscopy, as they are able to detect TB more accurately, particularly in patients with paucibacillary disease and in people living with HIV. Importantly, some of these WHO-endorsed tests can detect mycobacterial gene mutations associated with anti-TB drug resistance, allowing clinicians to tailor effective TB treatment. Currently, a wide array of molecular tests for TB detection is being developed and evaluated, and while some tests are intended for reference laboratory use, others are being aimed at the point-of-care and peripheral health care settings. Notably, there is an emergence of molecular tests designed, manufactured, and rolled out in countries with high TB burden, of which some are explicitly aimed for near-patient placement. These developments should increase access to molecular TB testing for larger patient populations. With respect to drug susceptibility testing, NAATs and next-generation sequencing can provide results substantially faster than traditional phenotypic culture. Here, we review recent advances and developments in molecular tests for detecting TB as well as anti-TB drug resistance.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".