Diagnosis of Multidrug‐Resistant Tuberculosis and Extensively Drug‐Resistant Tuberculosis: Current Standards and Challenges
Bibliographic record
Abstract
INTRODUCTION: The emergence of multidrug-resistant tuberculosis (MDR-TB) and, more recently, extensively drug-resistant TB (XDR-TB) is widely considered a serious threat to global TB control. Over 400,000 new cases of MDR-TB occur each year and, although their rates are currently unknown, XDR-TB cases have been detected in every country where there is capacity to detect them (including Canada). METHODS: The present article provides a narrative overview of the various diagnostic options available for XDR-TB, including conventional tools and newer rapid tests for drug resistance. Available data suggest that automated liquid cultures are highly accurate and their use is rapidly expanding. Newly developed phenotypic tests include TK Medium (Salubris Inc, USA), microscopic-observation drug-susceptibility assay, FASTPlaque-Response bacteriophage assay (Biotec Laboratories Ltd, UK), colorimetric redox indicator methods and the microcolony method. These tests are usually cheaper but not always simple to perform, with some requiring high standards of biosafety and quality control. Among the newly developed phenotypic methods, reverse hybridization-based assays, referred to as line probe assays, represent a useful tool because of their superior accuracy and cost-effectiveness. CONCLUSIONS: To effectively address the threats of MDR-TB and XDR-TB, global initiatives are required to scale-up culture and drug susceptibility testing capacities, especially in high-burden countries where such capacity is scarce. In parallel, efforts are needed to expand the use of novel and emerging technologies (ie, molecular diagnostics) for the rapid determination of 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 | 0.026 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| 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; 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".