Accuracy of line probe assays for the diagnosis of pulmonary and multidrug-resistant tuberculosis: a systematic review and meta-analysis
Bibliographic record
Abstract
Only 25% of multidrug-resistant tuberculosis (MDR-TB) cases are currently diagnosed. Line probe assays (LPAs) enable rapid drug-susceptibility testing for rifampicin (RIF) and isoniazid (INH) resistance and Mycobacterium tuberculosis detection. Genotype MTBDRplusV1 was WHO-endorsed in 2008 but newer LPAs have since been developed.This systematic review evaluated three LPAs: Hain Genotype MTBDRplusV1, MTBDRplusV2 and Nipro NTM+MDRTB. Study quality was assessed with QUADAS-2. Bivariate random-effects meta-analyses were performed for direct and indirect testing. Results for RIF and INH resistance were compared to phenotypic and composite (incorporating sequencing) reference standards. M. tuberculosis detection results were compared to culture.74 unique studies were included. For RIF resistance (21 225 samples), pooled sensitivity and specificity (with 95% confidence intervals) were 96.7% (95.6-97.5%) and 98.8% (98.2-99.2%). For INH resistance (20 954 samples), pooled sensitivity and specificity were 90.2% (88.2-91.9%) and 99.2% (98.7-99.5%). Results were similar for direct and indirect testing and across LPAs. Using a composite reference standard, specificity increased marginally. For M. tuberculosis detection (3451 samples), pooled sensitivity was 94% (89.4-99.4%) for smear-positive specimens and 44% (20.2-71.7%) for smear-negative specimens.In patients with pulmonary TB, LPAs have high sensitivity and specificity for RIF resistance and high specificity and good sensitivity for INH resistance. This meta-analysis provides evidence for policy and practice.
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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 | 0.018 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.035 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".