Xpert MTB/RIF Testing in a Low Tuberculosis Incidence, High-Resource Setting: Limitations in Accuracy and Clinical Impact
Bibliographic record
Abstract
BACKGROUND: Xpert MTB/RIF, the first automated molecular test for tuberculosis, is transforming the diagnostic landscape in low-income countries. However, little information is available on its performance in low-incidence, high-resource countries. METHODS: We evaluated the accuracy of Xpert in a university hospital tuberculosis clinic in Montreal, Canada, for the detection of pulmonary tuberculosis on induced sputum samples, using mycobacterial cultures as the reference standard. We also assessed the potential reduction in time to diagnosis and treatment initiation. RESULTS: We enrolled 502 consecutive patients who presented for evaluation of possible active tuberculosis (most with abnormal chest radiographs, only 18% symptomatic). Twenty-five subjects were identified to have active tuberculosis by culture. Xpert had a sensitivity of 46% (95% confidence interval [CI], 26%-67%) and specificity of 100% (95% CI, 99%-100%) for detection of Mycobacterium tuberculosis. Sensitivity was 86% (95% CI, 42%-100%) in the 7 subjects with smear-positive results, and 28% (95% CI, 10%-56%) in the remaining subjects with smear-negative, culture-positive results; in this latter group, positive Xpert results were obtained a median 12 days before culture results. Subjects with positive cultures but negative Xpert results had minimal disease: 11 of 13 had no symptoms on presentation, and mean time to positive liquid culture results was 28 days (95% CI, 25-47 days) compared with 14 days (95% CI, 8-21 days) in Xpert/culture-positive cases. CONCLUSIONS: Our findings suggest limited potential impact of Xpert testing in high-resource, low-incidence ambulatory settings due to lower sensitivity in the context of less extensive disease, and limited potential to expedite diagnosis beyond what is achieved with the existing, well-performing diagnostic algorithm.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.238 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".