QuantiFERON<sup>®</sup>-TB Gold assay for the diagnosis of latent tuberculosis infection
Bibliographic record
Abstract
Tuberculin skin test (TST) has been used for 100 years for the diagnosis of latent tuberculosis (TB) infection (LTBI). In recent years, increasing interest in the diagnosis of TB has led to the development of new assays. QuantiFERON-TB Gold (QFT-G) is an IFN-gamma-release assay that measures the release of interferon after stimulation in vitro by Mycobacterium tuberculosis antigens. The main advantage of this assay with respect to TST is the lack of crossreaction with bacillus Calmette-Guérin and most nontuberculous mycobacteria. QFT-G also eliminates the need for the patient to return for test reading in 48-72 h. In the immunocompromised host and in pediatric populations, studies suggest that the QFT-G better correlates with the risk of TB than the TST, but data remain inconclusive. In contrast to TST, there are no prospective studies regarding the association of the QFT-G result and the risk for development of TB. Given its advantages, the QFT-G may become the standard test for the diagnosis of LTBI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".