Risk factors for excess mortality and death in adults with tuberculosis in Western Kenya
Bibliographic record
Abstract
OBJECTIVES: To evaluate excess mortality and risk factors for death during anti-tuberculosis treatment in Western Kenya. METHODS: We abstracted surveillance data and compared mortality rates during anti-tuberculosis treatment with all-cause mortality from a health and demographic surveillance population to obtain standardised mortality ratios (SMRs). Risk factors for excess mortality were obtained using a relative survival model, and for death during treatment using a proportional hazards regression model. RESULTS: The crude mortality rate during anti-tuberculosis treatment was 18.0 (95%CI 16.8-19.2) per 100 person-years. The age and sex SMR was 8.8 (95%CI 8.2-9.4). Excess mortality was greater in human immunodeficiency virus (HIV) positive TB patients (excess hazard ratio [eHR] 2.1, 95%CI 1.5-3.1), and lower in patients who were female or started treatment in a later year. Mortality was high in patients with unknown HIV status (HR 2.9, 95%CI 2.2-3.8) or, if HIV-positive, not on antiretroviral treatment (ART; HR 3.3, 95%CI 2.5-4.5) or not known to be on ART (HR 2.8, 95%CI 2.1-3.7). The attributable fraction of incomplete uptake of HIV testing and ART on mortality was 31% (95%CI 15-45) compared to HIV-positive patients on ART. CONCLUSION: Increasing the uptake of HIV testing and ART would further reduce mortality during anti-tuberculosis treatment by an estimated 31%.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".