GenoType MTBDR assays for the diagnosis of multidrug-resistant tuberculosis: a meta-analysis
Bibliographic record
Abstract
The global extensively drug-resistant tuberculosis (TB) response plan calls for implementation of rapid tests to screen patients at risk of drug-resistant TB. Currently, two line probe assays exist, the INNO-LiPA(R)Rif.TB assay (Innogenetics, Ghent, Belgium) and the GenoType MTBDR assay (Hain LifeScience GmbH, Nehren, Germany). While LiPA studies have been reviewed, the accuracy of GenoType assays has not been systematically reviewed. The present authors carried out a systematic review and used meta-analysis methods appropriate for diagnostic accuracy. After the literature searches, 14 comparisons for rifampicin and 15 comparisons for isoniazid were identified in 10 articles that used GenoType MTBDR assays. Accuracy results were summarised in forest plots and pooled using bivariate random-effects regression. The pooled sensitivity (98.1%, 95% confidence interval (CI) 95.9-99.1) and specificity (98.7%, 95% CI 97.3-99.4) estimates for rifampicin resistance were very high and consistent across all subgroups, assay versions and specimen types. The accuracy for isoniazid was variable, with lower sensitivity (84.3%, 95% CI 76.6-89.8) and more inconsistent than specificity (99.5%, 95% CI 97.5-99.9). GenoType MDTBR assays demonstrate excellent accuracy for rifampicin resistance, even when used on clinical specimens. While specificity is excellent for isoniazid, sensitivity estimates were modest and variable. Together with data from demonstration projects, the meta-analysis provides evidence for policy making and clinical 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 | 0.020 | 0.032 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.064 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".