Drug concentration at the site of disease in children with pulmonary tuberculosis
Bibliographic record
Abstract
BACKGROUND: Current TB treatment for children is not optimized to provide adequate drug levels in TB lesions. Dose optimization of first-line antituberculosis drugs to increase exposure at the site of disease could facilitate more optimal treatment and future treatment-shortening strategies across the disease spectrum in children with pulmonary TB. OBJECTIVES: To determine the concentrations of first-line antituberculosis drugs at the site of disease in children with intrathoracic TB. METHODS: We quantified drug concentrations in tissue samples from 13 children, median age 8.6 months, with complicated forms of pulmonary TB requiring bronchoscopy or transthoracic surgical lymph node decompression in a tertiary hospital in Cape Town, South Africa. Pharmacokinetic models were used to describe drug penetration characteristics and to simulate concentration profiles for bronchoalveolar lavage, homogenized lymph nodes, and cellular and necrotic lymph node lesions. RESULTS: Isoniazid, rifampicin and pyrazinamide showed lower penetration in most lymph node areas compared with plasma, while ethambutol accumulated in tissue. None of the drugs studied was able to reach target concentration in necrotic lesions. CONCLUSIONS: Despite similar penetration characteristics compared with adults, low plasma exposures in children led to low site of disease exposures for all drugs except for isoniazid.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| 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".