Diagnostic accuracy of centralised assays for TB detection and detection of resistance to rifampicin and isoniazid: a systematic review and meta-analysis
Bibliographic record
Abstract
Various diagnostic companies have developed high throughput molecular assays for tuberculosis (TB) and resistance detection for rifampicin and isoniazid. We performed a systematic review and meta-analyses to assess the diagnostic accuracy of five of these tests for pulmonary specimens. The tests included were Abbott RealTime MTB, Abbott RealTime RIF/INH, FluoroType MTB, FluoroType MTDBR and BD Max MDR-TB assay. A comprehensive search of six databases for relevant citations was performed. Cross-sectional, case-control, cohort studies, and randomised controlled trials of any of the index tests were included. Respiratory specimens (such as sputum, bronchoalveolar lavage, tracheal aspirate,etc.) or their culture isolates. A total of 21 included studies contributed 26 datasets. We could only meta-analyse data for three of the five assays identified, as data were limited for the remaining two. For TB detection, the included assays had a sensitivity of 91% or more and the specificity ranged from 97% to 100%. For rifampicin resistance detection, all the included assays had a sensitivity of more than 92%, with a specificity of 99–100%. Sensitivity for isoniazid resistance detection varied from 70 to 91%, with higher specificity of 99–100% across all index tests. Studies that included head-to-head comparisons of these assays with Xpert MTB/RIF for detection of TB and rifampicin resistance suggested comparable diagnostic accuracy. In people with symptoms of pulmonary TB, the centralised molecular assays demonstrate comparable diagnostic accuracy for detection of TB, rifampicin and isoniazid resistance to Xpert MTB/RIF assay, a WHO recommended molecular test.
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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 | 0.022 | 0.052 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.034 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 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".