Accuracy of Xpert Ultra for the diagnosis of paediatric tuberculosis in a low TB burden country: a prospective multicentre study
Bibliographic record
Abstract
INTRODUCTION: Childhood pulmonary tuberculosis (TB) remains a diagnostic challenge. This study aimed to evaluate the performance of Xpert Ultra for the diagnosis of pulmonary TB in children in a low TB prevalence setting. METHODS: Prospective, multicentre, diagnostic accuracy study. Children with clinical or radiological suspicion of pulmonary TB were recruited at 11 paediatric units in Spain. Up to three gastric or sputum specimens were taken on 3 consecutive days, and analysed by Xpert MTB/RIF, Xpert Ultra and culture in parallel. RESULTS: 86 children were included (median age 4.9 years, IQR 2.0-10.0; 51.2% male). The final diagnosis was pulmonary TB in 75 patients (87.2%); 33 (44.0%) were microbiologically confirmed. A total of 219 specimens, comprising gastric aspirates (n=194; 88.6%) and sputum specimens (n=25; 11.4%), were analysed. Using culture as reference standard and comparing individual specimens, the sensitivity was 37.8% (14/37) for Xpert MTB/RIF and 81.1% (30/37) for Xpert Ultra (p<0.001); specificity was 98.4% (179/182) and 93.4% (170/182), respectively (p=0.02). In the per-patient analysis, considering positive results on any specimen, the sensitivity was 42.9% (9/21) for Xpert MTB/RIF and 81.0% for Xpert Ultra (17/21, p=0.01); specificity was 96.9% (63/65) and 87.7% (57/65, p=0.07), respectively. CONCLUSIONS: In children with pulmonary TB in a low burden setting, Xpert Ultra has significantly higher sensitivity than the previous generation of Xpert assay and only marginally lower specificity. Therefore, in children undergoing evaluation for suspected pulmonary TB, Xpert Ultra should be used in preference to Xpert MTB/RIF whenever possible.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".