Latent Tuberculosis Infection Screening and 2-Year Outcome in Antiretroviral-Naive HIV-Infected Patients in a Low-Prevalence Country
Bibliographic record
Abstract
BACKGROUND: Diagnosis and treatment of latent tuberculous infection decrease the incidence of tuberculosis (TB) in HIV-infected patients. OBJECTIVES: To evaluate the diagnostic yield of two IFN-γ release assays and tuberculin skin testing for the screening of latent infection in HIV-infected patients. METHODS: We performed a prospective study in 29 referral centers for HIV care in France. Asymptomatic, antiretroviral-naive patients infected with HIV-1 who consented to participate underwent two commercial tests (T-SPOT.TB and QuantiFERON-TB Gold In-Tube ELISA test [QFT]) and skin test at enrollment and were followed up for clinical events during 24 months. RESULTS: Between March 2009 and 2011, 506 patients were included, of whom 415 performed the three tests. Median age was 38 years (interquartile range, 31-45 yr), with median CD4 cell count of 466/μL (337-615 μL), and HIV viral load of 4.5 log10 copies/ml (3.6-4.9 log10 copies/ml). At least one IFN-γ release assay was positive for 55 (13.5%) patients: QFT (n = 43), T-SPOT.TB (n = 34), both (n = 22). Skin test was positive (>5 mm) in 66 (15.9%) patients, with intertest agreement at 81 to 86%. On multivariate analysis, positive IFN-γ release assay was only correlated with country of birth (8.4% for France vs. 17.9% for high-prevalence countries, P = 0.004). Of the 55 patients with positive IFN-γ release assay, 8 (14.5%) developed active TB, all within 120 days. No other case of active TB was diagnosed. Once active TB was excluded, IFN-γ release assay-based latent infection prevalence was 11.8%. CONCLUSIONS: Systematic screening for latent TB infection by IFN-γ release assay identifies a population at high risk of active TB over the next months. An extensive diagnostic work-up for active TB must follow positive IFN-γ release assay, before considering treatment of latent infection. Clinical trial registered with www.clinicaltrials.gov (NCT00805272).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".