Concordance between tuberculin skin test and interferon-gamma release assay for latent tuberculosis screening in inflammatory bowel disease
Bibliographic record
Abstract
BACKGROUND/AIMS: Latent tuberculosis screening is mandatory prior to initiating anti-tumor necrosis factor (anti-TNF) medications. Guidelines recommend interferon-gamma release assays (IGRA) as first line screening method for the general population. Studies provided conflicting evidence on IGRA and tuberculin skin test (TST) performance in inflammatory bowel disease (IBD) patients. We assessed test concordance and the effects of immunosuppression on their performance in IBD patients. METHODS: We searched MEDLINE, Embase and Cochrane databases (2011-2018) for studies testing TST and IGRA in IBD. Primary outcome was TST and IGRA concordance. Secondary outcomes were effects of immunosuppressive therapy on performance. Immunosuppression defined as either steroids, thiopurine, methotrexate or cyclosporine use. We used the pooled random effects model to adjust for heterogeneity analyzed using (I2-Q statistics). We compared the fixed model to exclude smaller study effects. RESULTS: Sixteen studies (2,488 patients) were included. Pooled TST and IGRA concordance was 85% (95% confidence interval [CI], 81%-88%; P=0.01). Effects of immunosuppression were reported in 8 studies (814 patients). The odds ratio of testing positive by IGRA decreased to 0.57 if immunosuppressed (95% CI, 0.31-1.03; P=0.06). The odds ratio of testing positive by TST if immunosuppressed was 1.14 (95% CI, 0.61-2.12; P=0.69). The fixed model yielded similar results, however the negative effect of immunosuppression on IGRA reached statistical significance (P=0.01). CONCLUSIONS: While concordance was 85% between TST and IGRA, the performance of IGRA seems to be negatively affected by immunosuppression. Given the importance of detecting latent tuberculosis prior to anti-TNF initiation, further randomized controlled trials comparing the performance of TST and IGRA in IBD patients are needed.
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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 | 0.045 | 0.176 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.011 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".