Comparing the Diagnostic Performance of QuantiFERON-TB Gold Plus to Other Tests of Latent Tuberculosis Infection: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND: We conducted a review to compare the sensitivity, specificity, reproducibility, and predictive ability of QuantiFERON-TB Gold Plus (QFT-Plus) with that of QuantiFERON-TB Gold In-Tube (QFT-GIT; QIAGEN, Hilden, Germany) and other latent tuberculosis infection (LTBI) tests. METHODS: We searched MEDLINE, Embase, Web of Science, and the Cochrane Database of Systematic Reviews from January 2013 through May 2020. We included studies comparing QFT-Plus with at least one other LTBI test. We estimated sensitivity from studies of patients with active tuberculosis, and specificity from studies of healthy individuals with low risk of LTBI. Three independent reviewers evaluated eligibility, extracted data, and assessed risk of bias. RESULTS: Compared with QFT-GIT, the sensitivity of QFT-Plus in patients with active TB was 1.3% higher (95% confidence interval [CI], -0.3% to 2.9%); in 2 studies of patients with very low probability of LTBI, the specificity was 0.9% lower (95% CI, -2.4% to 0.6%). These differences were not statistically significant. The agreement between QFT-Plus and QFT-GIT was high, with a pooled Cohen's kappa statistic of 0.83 (95% CI, 0.79 to 0.88). The reproducibility of QFT-GIT and QFT-Plus was similarly poor. All participants in the studies to estimate sensitivity were aged ≥15 years, and only 6 were people living with human immunodeficiency virus. We found no studies to assess predictive ability. CONCLUSIONS: QFT-Plus has diagnostic performance that is very similar to that of QFT-GIT. Further studies are needed to assess the sensitivity of QFT-Plus in immunocompromised patients and younger children before concluding if this new version offers advantages.
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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 | 0.027 | 0.083 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.027 | 0.035 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".