Tuberculosis Screening and Active Tuberculosis among HIV‐Infected Persons in a Canadian Tertiary Care Centre
Bibliographic record
Abstract
RATIONALE: HIV infection increases the risk of reactivation of latent tuberculosis (TB). The present study evaluates how latent TB is detected and treated to determine the effectiveness of screening in HIV-infected patients with diverse risk profiles. METHOD: A retrospective medical record database review (1988 to 2007) was conducted at a tertiary care HIV clinic. The proportion of patients receiving tuberculin skin tests (TSTs) and the rate of active TB at each stage of screening and prevention were estimated. Predictors of receiving a TST at baseline, testing positive by TST and developing active TB were evaluated. RESULTS: In the present study, 2123 patients were observed for a total of 9412 person-years. Four hundred seventy-six (22.4%) patients were tested by TST within 90 days of first clinic visit. Having a first clinic visit during the highly active antiretroviral therapy era (OR 3.64; 95% CI 2.66 to 4.99), country of birth (ORs: Africa 3.11, Asia 2.79, Haiti 3.14, and Latin America and the Caribbean 2.38), time between HIV diagnosis and first visit (OR per one-year change 0.97; 95% CI 0.94 to 0.99) and previous antiretroviral exposure (OR 0.61; 95% CI 0.45 to 0.81) were independent predictors of receiving a TST at baseline. Of the 17 patients who developed active TB during follow-up, nine (53%) had no documented TSTs at baseline or during follow-up. Forty-one per cent of all TB patients and 56% of TB patients who were not screened were born in Canada. CONCLUSION: The administration of TSTs to newly diagnosed HIV patients was inconsistent and differential according to country of birth, among other factors, resulting in missed opportunities for TB prevention.
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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 | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 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) | 0.004 | 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".