Positive Conversion of Tuberculin Skin Test and Performance of Interferon Release Assay to Detect Hidden Tuberculosis Infection During Anti-Tumor Necrosis Factor Agent Trial
Bibliographic record
Abstract
OBJECTIVES: To evaluate tuberculin skin tests (TST) and interferon-gamma (IFN-gamma) assay in the detection of latent tuberculosis (TB) infection during tumor necrosis factor (TNF) antagonist treatment in Korean patients with initial negative TST result. METHODS: Eighty-six patients with rheumatic diseases who had received anti-TNF agents for over one year were investigated. Clinical data were obtained from medical records. All patients received followup TST, and IFN-gamma assay was performed in 64. RESULTS: The study population consisted of 40 rheumatoid arthritis (RA), 34 ankylosing spondylitis (AS), 9 juvenile rheumatoid arthritis (JRA), and 3 other patients. The TST converted to positive in 28 (32.6%) patients. There was no significant variation between TST conversion rate and all risk factors. Although there was no statistical significance, the odds of the TST conversion rate tended to increase with the duration of TNF antagonist administration. Nine (14.1%) of 64 patients who performed an IFN-gamma assay had positive results. Among 28 TST positive conversion cases, 4 patients with AS and 1 with psoriatic arthritis had positive IFN-gamma assay results, and one of them developed miliary TB. However, none of the 4 RA patients with positive IFN-gamma assay showed TST conversion. There was 68.6% agreement (kappa = 0.29, p = 0.02) between TST and IFN-gamma assay results. CONCLUSION: Serial TST with IFN-gamma assay may be useful to identify false-negative response to cases of latent Mycobacterium tuberculosis infection and new TB infections in patients with immune mediated inflammatory diseases during longterm anti-TNF therapy, especially in areas with intermediate TB burden.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".