Bacteriophage assays for rifampicin resistance detection in Mycobacterium tuberculosis: updated meta-analysis.
Bibliographic record
Abstract
OBJECTIVE: To update a previously reported meta-analysis of evidence regarding the diagnostic accuracy and performance characteristics of commercial and non-commercial phage-based assays for the detection of rifampicin (RMP) resistant tuberculosis (TB). DESIGN AND OUTCOMES: We conducted a systematic review and meta-analysis of test accuracy using bivariate random effects regression and hierarchical summary receiver operating characteristics (HSROC) analysis. Tests included the commercial FASTPlaque assays, luciferase reporter phage (LRP) assays, and in-house phage amplification tests. Sensitivity and specificity for RMP resistance were the main outcomes. RESULTS: By updating previous literature searches, a total of 31 studies (with 3085 specimens) were included in this meta-analysis. Evaluations of commercial phage amplification assays yielded more variable estimates of sensitivity (range 81-100%) and specificity (range 73-100%) compared to evaluations of in-house amplification assays (sensitivity range 88-100%, specificity range 84-100%). LRP evaluations yielded the most consistent estimates of diagnostic accuracy, with seven of eight studies reporting 100% sensitivity and four of eight reporting 100% specificity. Estimates of accuracy failed to capture a major failing of the commercial assay, i.e., the rate of contaminated and indeterminate results. These ranged from 3% to 36% in studies looking at direct detection of RMP resistance from patient specimens (mean 20%). CONCLUSION: Phage-based assays will require further development to maximise interpretable results and reduce technical failures. Once technical issues are resolved, impact on patient-important outcomes and cost-effectiveness need to be determined to inform policy for widespread use.
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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 | 0.031 | 0.062 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.049 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".