Systematic Review of the Performance of Rapid Rifampicin Resistance Testing for Drug-Resistant Tuberculosis
Bibliographic record
Abstract
INTRODUCTION: Rapid tests for rifampicin resistance may be useful for identifying isolates at high risk of drug resistance, including multidrug-resistant TB (MDR-TB). However, choice of diagnostic test and prevalence of rifampicin resistance may both impact a diagnostic strategy for identifying drug resistant-TB. We performed a systematic review to evaluate the performance of WHO-endorsed rapid tests for rifampicin resistance detection. METHODS: We searched MEDLINE, Embase and the Cochrane Library through January 1, 2012. For each rapid test, we determined pooled sensitivity and specificity estimates using a hierarchical random effects model. Predictive values of the tests were determined at different prevalence rates of rifampicin resistance and MDR-TB. RESULTS: We identified 60 publications involving six different tests (INNO-LiPA Rif. TB assay, Genotype MTBDR assay, Genotype MTBDRplus assay, Colorimetric Redox Indicator (CRI) assay, Nitrate Reductase Assay (NRA) and MODS tests): for all tests, negative predictive values were high when rifampicin resistance prevalence was ≤ 30%. However, positive predictive values were considerably reduced for the INNO-LiPA Rif. TB assay, the MTBDRplus assay and MODS when rifampicin resistance prevalence was < 5%. LIMITATIONS: In many studies, it was unclear whether patient selection or index test performance could have introduced bias. In addition, we were unable to evaluate critical concentration thresholds for the colorimetric tests. DISCUSSION: Rapid tests for rifampicin resistance alone cannot accurately predict rifampicin resistance or MDR-TB in areas with a low prevalence of rifampicin resistance. However, in areas with a high prevalence of rifampicin resistance and MDR-TB, these tests may be a valuable component of an MDR-TB management strategy.
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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 | 0.014 | 0.075 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".