Kinetics of the QuantiFERON<sup>®</sup>-TB Gold In-Tube test during treatment of patients with sputum smear-positive tuberculosis in relation to initial TST result and severity of disease
Bibliographic record
Abstract
The QuantiFERON-TB Gold In-Tube test (QFN) measures interferon-gamma production in response to Mycobacterium tuberculosis antigens. Our aim was to assess the kinetics of the QFN and initial tuberculin skin test (TST) result in relation to severity of disease in a tuberculosis (TB) endemic area. Smear-positive TB patients (n = 71) were recruited at Gondar University Hospital, Ethiopia. The TST, QFN, CD4+ cell count and clinical symptoms (TB score) were assessed and followed up during treatment. From baseline to 7 months after treatment, there was a significant decrease in QFN reactivity (93.8% to 62.5% in HIV-negative/TB; 70.3% to 33.3% in HIV-positive/TB patients) down to a level comparable to a control group of blood donors (51.2%). The agreement between TST and QFN was poor in TB patients compared to healthy controls. A negative TST correlated to more advanced TB in contrast to a negative QFN test. We conclude that the QFN reactivity is significantly reduced at the end of treatment against active TB to the background level of healthy blood donors, and that the agreement between TST and QFN is poor including correlation to the severity of disease.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".