Fourth-Generation QuantiFERON-TB Gold Plus: What Is the Evidence?
Bibliographic record
Abstract
QuantiFERON-TB Gold Plus (QFT-Plus) is the latest generation of interferon gamma release assays (IGRAs) to receive approval from the U.S. FDA, replacing its predecessor, QuantiFERON-TB Gold In-Tube (QFT-GIT). The novelty of QFT-Plus is that it elicits a response from CD8 T cells, in addition to CD4 T cells, thus collecting a broader response from T-cell subsets than QFT-GIT. It was developed with the aim to improve the detection of latent tuberculosis infection (LTBI), especially among recently exposed contacts, immunocompromised hosts, and young children. In this minireview, we summarize the performance of QFT-Plus compared with that of QFT-GIT among active tuberculosis (TB) patients (a surrogate for LTBI patients), high-risk populations, and low-risk individuals based on recent publications. Studies comparing QFT-Plus to QFT-GIT currently do not support the superior performance of QFT-Plus in individuals with active TB and LTBI. The difference in sensitivity between QFT-Plus and QFT-GIT in active TB patients was not significant in nearly all studies and ranged from -4.0 to 2.0%. Among high-risk groups, the agreement between QFT-Plus and QFT-GIT was 89.9 to 96.0% (kappa coefficient range, 0.80 to 0.91). The specificity in the low-risk population was slightly lower for QFT-Plus than for QFT-GIT, with the difference ranging from -7.4 to 0%. Further studies are needed to accurately evaluate the sensitivity of QFT-Plus in immunocompromised hosts and children. In addition, further evidence is required to validate a modified interpretation of QFT-Plus for the identification of false-positive results in low-risk health care workers.
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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 | 0.023 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".