Added Value of QuantiFERON TB-Gold in-Tube for Detecting Latent Tuberculosis Infection among Persons Living with HIV/AIDS
Bibliographic record
Abstract
OBJECTIVE: To evaluate the added value of QuantiFERON TB-Gold in-Tube (QTF-GIT) over the tuberculin skin testing (TST) for detecting latent tuberculosis (TB) infection (LTBI) among patients with AIDS in a city with a low TB incidence rate (11.1/100,000 inhabitants) and universal BCG coverage. METHODS: Three hundred consecutive patients with AIDS in eight outpatient sexually transmitted disease public clinics in Brasilia were submitted to QFT-IT and TST between May 2011 and March 2013. A positive result of either test was considered to be LTBI. RESULTS: Median CD4-cell count was 477.5 cells/mm(3); 295 (98.3%) were using antiretroviral therapy. Eighteen patients (6%, 95% CI: 3.6%-9.3%) had LTBI, of whom 4 (1.3%, 95% CI: 0.04%-2.63%) had only a positive TST, 8 (2.7%, 95% CI: 0.8%-4.5%) had only a QFT-GIT positive test, and 6 (2%, 95% CI: 0.4%-3.6%) had positive results for both tests. This represents an 81.8% relative increase in LTBI detection when QFT-GIT is added to TST. The concordance between both tests was 96% (k = 0.48). CONCLUSIONS: The QFT-GIT alone was more effective to detect LTBI than TST alone and had an 81% added value as an add-on sequential test in this population with mild immunosuppression. The cost-effectiveness of these strategies remains to be evaluated.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".