Pooling sputum samples for Xpert® MTB/RIF and Xpert® Ultra testing for TB diagnosis
Bibliographic record
Abstract
BACKGROUND: The use of molecular amplification as-says for TB diagnosis is limited by their costs and cartridge stocks. Pooling multiple samples to test them together is reported to have similar accuracy to individual testing and to save costs. METHODS: Two surveys of individuals with presumptive TB were conducted to assess the performance of pooled testing using Xpert® MTB/RIF (MTB/RIF) and Xpert® Ultra (Ultra). RESULTS: A total of 500 individuals were tested using MTB/RIF, with 72 (14.4%) being MTB-positive. The samples were tested in 125 pools, with 50 pools having 1 MTB-positive and 75 only MTB-negative samples: 46/50 (92%, 95% CI 80.8–97.8) MTB-positive pools tested MTB-positive and 71/75 (94.7%, 95% CI 86.9–98.5) MTB-negative pools tested MTB-negative in the pooled test (agreement: 93.6%, κ = 0.867). Five hundred additional samples were tested using Ultra, with 60 (12%) being MTB-positive. Samples were tested in 125 pools, with 42 having 1 MTB-positive and 83 only MTB-negative samples: 35/42 (83.6%, 95% CI 68.6–93.0) MTB-positive pools tested MTB-positive and 82/83 (98.8%, 95% CI 93.5–100.0) MTB-negative pools tested MTB-negative in the pooled test (agreement: 93.6%, κ = 0.851; P > 0.1 between individual and pooled testing). Pooled testing saved 35% (MTB/RIF) and 46% (Ultra) of cartridges. CONCLUSIONS: Pooled and individual testing has a high level of agreement and improves testing efficiency.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".