Utility of the QuantiFERON<SUP>®</SUP>-TB Gold In-Tube assay for the diagnosis of tuberculosis in Moroccan children
Bibliographic record
Abstract
SETTING: The utility of interferon-gamma release assays (IGRAs), such as the QuantiFERON-TB Gold In-Tube (QFT-GIT) test, in diagnosing active tuberculosis (TB) in children is unclear and depends on the epidemiological setting. OBJECTIVE: To evaluate the performance of QFT-GIT for TB diagnosis in children living in Morocco, an intermediate TB incidence country with high bacille Calmette-Gurin vaccination coverage. DESIGN: We prospectively recruited 109 Moroccan children hospitalised for clinically suspected TB, all of whom were tested using QFT-GIT. RESULTS: For 81 of the 109 children, the final diagnosis was TB. The remaining 28 children did not have TB. QFT-GIT had a sensitivity of 66% (95%CI 5277) for the diagnosis of TB, and a specificity of 100% (95%CI 88100). The tuberculin skin test (TST) had lower sensitivity, at 46% (95%CI 3360), and its concordance with QFT-GIT was limited (69%). Combining QFT-GIT and TST results increased sensitivity to 83% (95%CI 6992). CONCLUSION: In epidemiological settings such as those found in Morocco, QFT-GIT is more sensitive than the TST for active TB diagnosis in children. Combining the TST and QFT-GIT would be useful for the diagnosis of active TB in children, in combination with clinical, radiological and laboratory data.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".