Childhood Tuberculosis: Epidemiology, Etiology, Pathophysiology, Pathogenesis, Risk Factors, Prevention and Diagnosis: A Narrative Review
Bibliographic record
Abstract
Background: Tuberculosis is a chronic infectious disease caused by Mycobacterium tuberculosis bacteria. These bacteria are rod-shaped and acid-resistant, so they are often known as acid-resistant bacilli (BTA). Children are at high risk of TB infection, especially infants and toddlers. Children infected with TB are at risk of developing severe TB disease that can lead to death or long-term disability. This article review focuses on a brief introduction to anemia, its etiology, pathophysiology, impact and preventive measures. Methods: Major databases including Scopus, Pubmed, Proquest, Google Scholar, and Science Direct were searched for articles related to pediatric tuberculosis. The keyword used in the literature search was “pediatric tuberculosis”. The time frame of the articles obtained ranged from 2016 to 2023. Results: This study shows that the burden of pediatric tuberculosis (TB) remains high globally and nationally. WHO reported 1.2 million cases of pediatric TB with more than 200,000 deaths, mainly in children under five. In Indonesia, pediatric TB cases increased from 110,881 (2022) to 129,798 (2023), with bacteriological confirmation still low, especially in children <5 years. In South Sulawesi, the trend of cases is decreasing, but in Parepare City it has increased from 16 to 21 cases. The main obstacles include difficulty in diagnosis, limited facilities, low coverage of TB preventive therapy (TPT), and suboptimal handling of drug-resistant TB. Conclusion: Diagnosing TB in children remains difficult due to limited tools and low bacterial presence. Many cases go underdiagnosed or overdiagnosed, while drug-resistant TB poses added risks. Prevention includes BCG vaccination, controlling risk factors, and TPT. Disparities in diagnosis and treatment persist. Early detection, family education, and access to care are vital. Strengthening community and household approaches can narrow diagnostic gaps and prevent complications in childhood TB.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".