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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".