Tuberculosis Disease in Children and Adolescents on Therapy With Antitumor Necrosis Factor-ɑ Agents: A Collaborative, Multicenter Paediatric Tuberculosis Network European Trials Group (ptbnet) Study
Bibliographic record
Abstract
BACKGROUND: In adults, anti-tumor necrosis factor-α (TNF-α) therapy is associated with progression of latent tuberculosis (TB) infection (LTBI) to TB disease, but pediatric data are limited. METHODS: Retrospective multicenter study within the Paediatric Tuberculosis Network European Trials Group, capturing patients <18 years who developed TB disease during anti-TNF-α therapy. RESULTS: Sixty-six tertiary healthcare institutions providing care for children with TB participated. Nineteen cases were identified: Crohn's disease (n = 8; 42%) and juvenile idiopathic arthritis (n = 6; 32%) were the commonest underlying conditions. Immune-based TB screening (tuberculin skin test and/or interferon-γ release assay) was performed in 15 patients before commencing anti-TNF-α therapy but only identified 1 LTBI case; 13 patients were already receiving immunosuppressants at the time of screening. The median interval between starting anti-TNF-α therapy and TB diagnosis was 13.1 (IQR, 7.1-20.3) months. All cases presented with severe disease, predominantly miliary TB (n = 14; 78%). One case was diagnosed postmortem. TB was microbiologically confirmed in 15 cases (79%). The median duration of anti-TB treatment was 50 (IQR, 46-66) weeks. Five of 15 (33%) cases who had completed TB treatment had long-term sequelae. CONCLUSIONS: LTBI screening is frequently false-negative in this patient population, likely due to immunosuppressants impairing test performance. Therefore, patients with immune-mediated diseases should be screened for LTBI at the point of diagnosis, before commencing immunosuppressive medication. Children on anti-TNF-α therapy are prone to severe TB disease and significant long-term morbidity. Those observations underscore the need for robust LTBI screening programs in this high-risk patient population, even in low-TB-prevalence settings.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".