Prevalence and Physical Environmental Conditions as Risk Factor for Pulmonary Tuberculosis in Indonesia 2015
Bibliographic record
Abstract
OBJECTIVE: The number of tuberculosis (TB) cases in Indonesia is currently very high, so the analysis is needed to describe the environmental conditions at risk of TB disease. The aim of the study was to look at the prevalence of pulmonary TB in 2013-2014 in Indonesia based on the area of residence of the respondents and to see the relationship between the environmental conditions of the prevalence of TB in Indonesia. MATERIAL & METHODS: The Prevalence SPTB 2013-2014 was used cross-sectional design with national coverage. Sampling selection used multi-staged cluster sampling in the population aged 15 years and above. The analysis data used SPSS program; first analysis was used bivariate and continuing to multivariate analysis. RESULT: Tb prevalence rate with bacteriological confirmed was 759 [95% CI: 590, 961] per 100,000 population in aged 15 years and above. The bivariable analysis shown those participant who live at house with floor <8m2/person [95% CI:1,053,1,710] and those participant who lived in house with kitchen was not separated from the main living area in the house [95% CI: 1,034,1,669], that was significant related with TB. In the multivariable model, the density characterized by family members with a floor surface <8m2/person [95% CI: 1,017,1,671]is at risk of developing TB. CONCLUSION: This study shows that the effect of the physical environment of living in a crowded household can be a risk factor for TB transmission. The other factor might be influence of infection Tb in the community.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".