Recent advances in the diagnosis of childhood tuberculosis
Bibliographic record
Abstract
Children account for a major proportion of the global tuberculosis disease burden, especially in endemic areas. However, the accurate diagnosis of childhood tuberculosis remains a major challenge. This review provides an overview of the most important recent advances in the diagnosis of intrathoracic childhood tuberculosis: (1) symptom-based approaches, including symptom-based screening of exposed children and symptom-based diagnosis of active disease; (2) novel immune-based approaches, including T cell assays and novel antigen-based tests; and (3) bacteriological and molecular methods that are more rapid and/or less expensive than conventional culture techniques for tuberculosis diagnosis and/or drug-resistance testing. Recent advances have improved our ability to diagnose latent infection and active tuberculosis in children, but establishing a diagnosis of either latent infection or active disease in HIV-infected children remains a major challenge, particularly in high-burden settings. Although improved access to diagnosis and treatment is essential, ultimately the burden of childhood tuberculosis is determined by the level of epidemic control achieved in a particular community. Several recent initiatives, in particular the United Nations Millennium Developmental Goals, deal with the problem of poverty and disease in a holistic fashion, but global political commitment is required to support these key initiatives.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".