The Relationship between External and Internal Risk Factors with Pulmonary Tuberculosis in Children Aged 0-59 Months in Slums in Indonesia, 2013
Bibliographic record
Abstract
BACKGROUND: Tuberculosis (Tb) remains a health problem throughout the world. World Health Organization (WHO) has set it as a “Global Emergency” disease. The difficulty of confirming the diagnosis of it in children, different from it in adults, causes the treatment of it in children often neglected. This problem is exacerbated by the supporting environmental conditions, namely living in slums, which makes the risk of transmission even higher. OBJECTIVE: To identify internal and external factors related to Tb in children aged 0-59 months living in slums in Indonesia. MATERIAL & METHOD: The data source used was the 2013 Basic Health Research (Riskesdas) using a sample of children less than five years old who lived in slums in 34 provinces in Indonesia. RESULTS: Logistic regression analysis found three risk factors, namely BCG immunization status (age-based), at-risk home environment, and gender (residential area-based). Vaccinated children under one year of age have the best probability of not suffering from pulmonary tuberculosis. Those who live in a house inhabited by less than five people, or in that occupied by more than four with no one of which smokes or does not suffer from pulmonary tuberculosis has a probability of not being exposed to it. Likewise, women who live in rural areas have almost two times less probability of suffering from it, compared to men in urban areas. CONCLUSION: Factors contributing to the prevalence of pulmonary tuberculosis in infants in slums are the status of BCG immunization, air cleanliness in the neighborhood, which can be seen from the differences of risks in rural and urban, and the number of inhabitants per house and their behavior.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".