Screening for Latent Tuberculosis Infection in Patients with Chronic Inflammatory Arthritis: Discrepancies Between Tuberculin Skin Test and Interferon-γ Release Assay Results
Bibliographic record
Abstract
OBJECTIVE: Screening for latent tuberculosis infection (LTBI) is mandatory before initiating biologics in patients with chronic inflammatory arthritis (CIA). However, few studies have evaluated the discrepancies between the results of tuberculin skin test (TST) and interferon-γ release assays (IGRA) in these patients. The purpose of our study was to investigate factors associated with TST and IGRA results in a large cohort of patients with CIA before the introduction of biologics. METHODS: A total of 563 consecutive patients with CIA (293 rheumatoid arthritis, 270 spondyloarthritis) and eligible for biologics were prospectively enrolled. Demographic, clinical, and biological data were recorded. Risk factors for LTBI were assessed. All patients underwent a TST, a chest radiograph, and an IGRA test (T-SPOT.TB). RESULTS: Agreement between the 2 tests was low (κ = 0.16). The bacillus Calmette-Guerin (BCG) status was significantly associated with discordance between the 2 tests (p = 0.004). The TST positivity rate was 34.8%. Factors associated with a negative TST were female sex (p = 0.02) and immunosuppressive treatment (p = 0.003). The only LTBI risk factor associated with TST positivity was an abnormal chest radiograph (p = 0.02). T-SPOT.TB was positive in 21.7% of patients and indeterminate in 15.6%. Previous active TB and chest radiograph abnormalities were associated with IGRA positivity (p = 0.008 and p = 3.9 × 10(-5), respectively). The BCG vaccination was associated with negative IGRA (p = 3 × 10(-4)). Indeterminate IGRA results were associated with age, C-reactive protein, and immunosuppressive treatment (p = 0.005, 0.007, and 0.004, respectively). CONCLUSION: Our data support the combined use of T-SPOT.TB and TST in patients with CIA before biologics introduction. However, despite these good diagnostic values, indeterminate results may complicate the use of IGRA.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".