Correlates of default from anti-tuberculosis treatment: a case study using Kenya's electronic data system
Bibliographic record
Abstract
BACKGROUND: In 2012, the World Health Organization estimated that there were 120,000 new cases and 9500 deaths due to tuberculosis (TB) in Kenya. Almost a quarter of the cases were not detected, and the treatment of 4% of notified cases ended in default. OBJECTIVE: To identify the determinants of anti-tuberculosis treatment default. DESIGN: Data from 2012 and 2013 were retrieved from a national case-based electronic data recording system. A comparison was made between new pulmonary TB patients for whom treatment was interrupted vs. those who successfully completed treatment. RESULTS: A total of 106,824 cases were assessed. Human immunodeficiency virus infection was the single most influential risk factor for default (aOR 2.7). More than 94% of patients received family-based directly observed treatment (DOT) and were more likely to default than patients who received DOT from health care workers (aOR 2.0). Caloric nutritional support was associated with lower default rates (aOR 0.89). Males were more likely to default than females (aOR 1.6). Patients cared for in the private sector were less likely to default than those in the public sector (aOR 0.86). CONCLUSION: Understanding the factors contributing to default can guide future program improvements and serve as a proxy to understanding the factors that constrain access to care among undetected cases.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".