Prevalence, incidence and mortality associated with tuberculosis in HIV-infected patients initiating antiretroviral therapy in rural Uganda
Bibliographic record
Abstract
BACKGROUND: Tuberculosis (TB) is the leading cause of death among people with HIV in sub-Saharan Africa. Expanding access to antiretroviral therapy (ART) may reduce the burden of TB, but to what extent is unknown. METHODS: In a study of 1044 adults who initiated home-based ART in Tororo, Uganda between 1 May 2003 and 30 June 2005, participants were screened for active TB at baseline and then monitored at weekly home visits. Participants with TB at baseline or follow-up were compared with those without TB to determine factors associated with mortality in those with TB. RESULTS: At baseline, 75 (7.2%) subjects had TB and a total of 53 (5.5%) were diagnosed with TB over a median of 1.4 years of follow-up (3.90 cases/100 person years). Cumulative mortality was 17.9/100 person-years for those with TB and 3.8/100 person-years for those without TB (P < 0.001). Mortality was associated with low baseline CD4 cell counts [relative hazard (RH), 0.99 per 1 cell/microl increase; P = 0.03] and marginally associated with a body mass index <or= 18 (RH, 2.04; P = 0.10) and increasing age (RH, 1.04 per year; P = 0.11). TB incidence and TB-associated mortality were highest within the first 6 months of ART and declined to 52% and 61% of expected values, respectively, from months 7 to 18 after ART initiation. CONCLUSION: TB remains an important cause of illness and death in patients receiving ART in Uganda. However, both appear to decline markedly, after 6 months of ART.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".