Risk Factors for Tuberculosis After Highly Active Antiretroviral Therapy Initiation in the United States and Canada: Implications for Tuberculosis Screening
Bibliographic record
Abstract
BACKGROUND: Screening for tuberculosis prior to highly active antiretroviral therapy (HAART) initiation is not routinely performed in low-incidence settings. Identifying factors associated with developing tuberculosis after HAART initiation could focus screening efforts. METHODS: Sixteen cohorts in the United States and Canada contributed data on persons infected with human immunodeficiency virus (HIV) who initiated HAART December 1995-August 2009. Parametric survival models identified factors associated with tuberculosis occurrence. RESULTS: Of 37845 persons in the study, 145 were diagnosed with tuberculosis after HAART initiation. Tuberculosis risk was highest in the first 3 months of HAART (20 cases; 215 cases per 100000 person-years; 95% confidence interval [CI]: 131-333 per 100000 person-years). In a multivariate Weibull proportional hazards model, baseline CD4+ lymphocyte count <200, black race, other nonwhite race, Hispanic ethnicity, and history of injection drug use were independently associated with tuberculosis risk. In addition, in a piece-wise Weibull model, increased baseline HIV-1 RNA was associated with increased tuberculosis risk in the first 3 months; male sex tended to be associated with increased risk. CONCLUSIONS: Screening for active tuberculosis prior to HAART initiation should be targeted to persons with baseline CD4 <200 lymphocytes/mm³ or increased HIV-1 RNA, persons of nonwhite race or Hispanic ethnicity, history of injection drug use, and possibly male sex.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".