Spatial Analysis of The Influence of Residential Density on The Spread of Tuberculosis Cases in Pasar Rebo General Hospital Service Area
Bibliographic record
Abstract
Background: Based on statistical data, in 2021 tuberculosis cases in the DKI Jakarta area reached 26,854, an increase of around 21% from 2020, 22,156 cases. Aims: This Study focuses on the lavel of administrative area whether a residential density shows significance in the spread of Pulmonary Turberculosis (TB) cases. Methods: The research carried out by a descriptive quantitative research in the Pasar Rebo General Hospital. Results: The distribution of patients in the Pasar Rebo General Hospital is not affected by the total population density found in a sub-district area. After the research was carried out in a smaller administrative scope, namely at the sub-district level, it was began to show a correlation between population density and the spread of pulmonary tuberculosis. Using a spatial approach, the research shows that there is a casual relationship between the cases of the spread of tuberculosis and the density of a residential area. Conclusion: Based on the data obtained and the spatial analysis, this study shows that the population density variable show the percentage level of the spread of a case of Pulmonary TB. But in this case it must be seen at a level of the smallest administrative area, namely at the sub-district level.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".