Timing of Antiretroviral Treatment, Immunovirologic Status, and TB Risk: Implications for Testing and Treatment
Bibliographic record
Abstract
BACKGROUND: Tuberculosis (TB) risk and mortality increase in the 6 months after highly active antiretroviral therapy (HAART) initiation. This short-term risk may be a consequence of HAART initiation and immune reconstitution. Alternatively, it may be due to confounding by low CD4 counts and high HIV viral loads (VLs). We assessed the TB risk before and after HAART initiation while appropriately controlling for time-updated laboratory values and HAART exposure. METHODS: We conducted an observational cohort study among persons enrolled in the North American AIDS Cohort Collaboration on Research and Design from 1998 through 2011. A marginal structural model was constructed to estimate the association of HAART initiation and TB risk. Inverse probability weights for the probability of HAART initiation were incorporated. RESULTS: Among 26,342 patients, 94 cases of TB were diagnosed during 147,557 person-years (p-y) of follow-up. The unadjusted TB rates were 93/100,000 p-y [95% confidence interval (CI): 63 to 132] before HAART initiation, 203/100,000 p-y (95% CI: 126 to 311) ≤6 months after HAART initiation, and 40/100,000 p-y (95% CI: 29 to 55) >6 months on HAART. After controlling for time-updated laboratory values, the adjusted odds of TB ≤6 months after HAART initiation and >6 months was 0.65 (95% CI: 0.28 to 1.51) and 0.29 (95% CI: 0.16 to 0.53), respectively. CONCLUSIONS: TB risk in the first 6 months after HAART initiation is not higher than that before HAART initiation after adjusting for CD4 count and VLs. These findings suggest that short-term TB risk may be related to low CD4 counts and high VLs near HAART initiation and support early HAART initiation to decrease TB risk.
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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 | 0.009 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".