Performance of interferon-gamma release assays in patients with inflammatory bowel disease: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Guidelines mandate screening for latent tuberculosis infection (LTBI) prior to anti-tumor necrosis factor (anti-TNF) therapy in patients with inflammatory bowel disease (IBD). However, many are already on immunosuppressive therapy (IST) that may affect the precision of the Tuberculin skin test (TST). Our aim was to assess the performance of the new interferon-gamma release assays (IGRAs) to detect LTBI in patients with IBD. METHODS: MEDLINE and EMBASE were searched (up to June 2011) to identify studies evaluating the performance of IGRAs (QuantiFERON-TB Gold [QFT-2G], QuantiFERON-TB Gold In-Tube [QFT-3G] and T-SPOT.TB) in individuals with IBD. Forest plots and pooled estimates using random effects models were created where applicable. RESULTS: Nine unique studies encompassing 1309 patients with IBD were included for analysis. The pooled concordance between the TST and QFT-2G/QFT-3G was 85% (95% confidence interval [CI] 77%-90%). The concordance of the TST and TSPOT.TB was 72% (95% CI 64%-78%). Studies assessing agreement reported more IGRA-/TST+ results versus IGRA+/TST- results. The pooled percentage of indeterminate results was 5% (95% CI 2%-9%) for QFT-2G/QFT-3G. TSPOT.TB showed similar results. Both positive QFT-2G/QFT-3G results (pooled odds ratio [OR] 0.37, 95% CI 0.16-0.87) and positive TST results (pooled OR 0.28, 95% CI 0.10-0.80) were significantly influenced by IST (both P = 0.02). CONCLUSIONS: While it remains difficult to determine superiority between the IGRAs and the TST, both are negatively affected by IST. Therefore, screening prior to initiation of IST should be considered. Nevertheless, it is imperative that all patients receive screening prior to anti-TNF therapy.
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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 | 0.016 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.035 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| 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".