Accuracy of Commercial Molecular Diagnostics for the Detection of Pulmonary Tuberculosis in China: A Systematic Review
Bibliographic record
Abstract
This systematic review assesses the accuracy of molecular diagnostic methods for the detection of pulmonary tuberculosis in studies performed in China, published in Chinese and English. We searched for studies that assessed the accuracy of molecular diagnostics for pulmonary TB in China in the China National Knowledge Infrastructure, the Wanfang Database, SinoMed, VIP Information, Pubmed, Embase, and the Cochrane Library. For each index test, a summary estimation for sensitivity and specificity was calculated using the bivariate random-effects model. A total of 59 studies were included in our analysis. Loop-mediated isothermal amplifcation (LAMP) assay (six studies; pooled sensitivity 90%, 95% CI 78-95%; specificity 93%, 85-97%), line probe assay (LPA) (one study; 87%, 84-90%; 94%, 92-95%) and polymerase chain reaction (PCR) (FQ-PCR and RT-PCR) (four studies; 90%, 55-99%; 93%, 71-99%) showed good diagnostic performance in the meta-analysis. The highest pooled sensitivity was from Xpert MTB/RIF (20 studies; pooled sensitivity 91%, 95% CI 87-94%). The highest pooled specificity was from cross-priming amplification (CPA) (six studies; pooled specificity 97%, 95-99%). The lowest pooled sensitivity and specificity were from simultaneous amplification and testing (SAT)-TB (three studies; 79%, 66-88%; 72%, 48-88%). In subgroup analysis, molecular diagnostics demonstrated higher sensitivity for pulmonary TB detection in smear-positive specimens. Xpert MTB/RIF, LAMP, LPA, CPA and PCR demonstrated high accuracy overall for pulmonary tuberculosis detection, while SAT-TB had poor performance.
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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 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".