Rapid diagnosis of drug-resistant TB using line probe assays: from evidence to policy
Bibliographic record
Abstract
Growing concerns about the spread of multidrug-resistant tuberculosis (MDR-TB) and the emergence of extensively drug-resistant TB have triggered substantial interest in the development and application of rapid tests for the detection of drug-resistant TB. Molecular assays to detect gene mutations that signal drug resistance are widely recognized as being most suited for rapid diagnosis. Among molecular assays, line probe assays have shown great promise. Currently, two line probe assays are commercially available: the INNO-LiPA Rif. TB kit (Innogenetics NV, Gent, Belgium) and the GenoType MTBDRplus assay (Hain Lifescience GmbH, Nehren, Germany). Evidence from a systematic review suggests that INNO-LiPA is a highly sensitive and specific test for the detection of rifampicin resistance in culture isolates. The test, however, appeared to have relatively lower sensitivity when used directly on clinical specimens. Another meta-analysis showed that the GenoType MTBDR assays had excellent accuracy for rifampicin resistance, even when used directly on clinical specimens. While specificity was excellent for isoniazid, sensitivity estimates were modest and variable. Based on evidence and expert opinion, the WHO recently endorsed the use of molecular line probe assays for rapid screening of patients at risk of MDR-TB. Special initiatives have been announced to make these assays accessible and affordable for countries with high MDR-TB prevalence. With strong evidence and new policy directives, the stage is now set for the use of rapid tests for MDR-TB diagnosis. Whether molecular tools, such as line probe assays, will actually make a clinical and public-health impact remains to be determined.
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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 | 0.124 | 0.284 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.007 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.015 | 0.025 |
| Open science | 0.011 | 0.008 |
| Research integrity | 0.020 | 0.017 |
| Insufficient payload (model declined to judge) | 0.015 | 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".