Challenges in Diagnosing Latent Tuberculosis Infection in Patients Treated with Tumor Necrosis Factor Antagonists
Bibliographic record
Abstract
Reactivation of latent tuberculosis infection (LTBI) is well recognized as an adverse event associated with anti-tumor necrosis factor-α (anti-TNF-α) therapy. The strengths and weaknesses of current techniques for detecting LTBI in patients with chronic inflammatory diseases such as rheumatoid arthritis (RA) and psoriasis have not been fully examined. T cell hyporesponsiveness due to immunosuppression caused by illness or drugs, referred to as anergy, may produce false-negative tuberculin skin test (TST) and interferon-γ release assay (IGRA) results. The literature suggests that anergy may influence screening performance of TST and IGRA tests in candidates for anti-TNF-α therapy. Conversely, the potential for false-positive TST and IGRA results must be considered, as treatment for LTBI may be associated with significant morbidity. This review examines the reliability issues related to LTBI diagnostic testing and provides practical direction to help prevent LTBI reactivation and facilitate successful anti-TNF-α treatment.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".