Reproducibility of Interferon Gamma (IFN-γ) Release Assays. A Systematic Review
Bibliographic record
Abstract
RATIONALE: Interferon gamma (IFN-γ) release assays for latent tuberculosis infection result in a larger-than-expected number of conversions and reversions in occupational screening programs, and reproducibility of test results is a concern. OBJECTIVES: Knowledge of the relative contribution and extent of the individual sources of variability (immunological, preanalytical, or analytical) could help optimize testing protocols. METHODS: We performed a systematic review of studies published by October 2013 on all potential sources of variability of commercial IFN-γ release assays (QuantiFERON-TB Gold In-Tube and T-SPOT.TB). The included studies assessed test variability under identical conditions and under different conditions (the latter both overall and stratified by individual sources of variability). Linear mixed effects models were used to estimate within-subject SD. MEASUREMENTS AND MAIN RESULTS: We identified a total of 26 articles, including 7 studies analyzing variability under the same conditions, 10 studies analyzing variability with repeat testing over time under different conditions, and 19 studies reporting individual sources of variability. Most data were on QuantiFERON (only three studies on T-SPOT.TB). A considerable number of conversions and reversions were seen around the manufacturer-recommended cut-point. The estimated range of variability of IFN-γ response in QuantiFERON under identical conditions was ±0.47 IU/ml (coefficient of variation, 13%) and ±0.26 IU/ml (30%) for individuals with an initial IFN-γ response in the borderline range (0.25-0.80 IU/ml). The estimated range of variability in noncontrolled settings was substantially larger (±1.4 IU/ml; 60%). Blood volume inoculated into QuantiFERON tubes and preanalytic delay were identified as key sources of variability. CONCLUSIONS: This systematic review shows substantial variability with repeat IFN-γ release assays testing even under identical conditions, suggesting that reversions and conversions around the existing cut-point should be interpreted with caution.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.006 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".