Mortality in a large tuberculosis treatment trial: modifiable and non-modifiable risk factors.
Bibliographic record
Abstract
SETTING: North America. OBJECTIVES: Tuberculosis (TB) patients in North America often have characteristics that may increase overall mortality. Identifying modifiable risk factors would allow for improvements in outcome. DESIGN: We evaluated mortality in a large TB treatment trial conducted in the United States and Canada. Persons with culture-positive pulmonary TB were enrolled after 2 months of treatment, treated for 4 more months under direct observation, and followed for 2 years (total observation: 28 months). Cause of death was determined by death certificate, autopsy, and/or clinical observation. RESULTS: Of 1075 participants, 71 (6.6%) died: 15/71 (21.1%) HIV-infected persons, and 56/1004 (5.6%) non-HIV-infected persons (P < 0.001). Only one death was attributed to TB. Cox multivariate regression analysis identified four independent risk factors for death after controlling for age: malignancy (hazard ratio [HR] 5.28, P < 0.0001), HIV (HR 3.89, P < 0.0001), daily alcohol (HR 2.94, P < 0.0001), and being unemployed (HR 1.99, P = 0.01). The risk of death increased with the number of independent risk factors present (P < 0.0001). Extent of disease and treatment failure/relapse were not associated with an increased risk of death. CONCLUSIONS: Death due to TB was rare. Interventions to treat malignancy, HIV, and alcohol use in TB patients are needed to reduce mortality in this patient population.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".