Screening and Monitoring of Latent Tubercular Infection in Patients Taking Tumor Necrosis Factor- Blockers for Psoriatic Arthritis
Bibliographic record
Abstract
Patients with arthritis who need treatment with biologics are carefully screened for possible previous exposure to tuberculosis to detect any latent tubercular infection (LTBI). The traditional method of screening for LTBI is not specific, because positivity could also depend on infection by atypical mycobacteria and bacillus Calmette-Guerin vaccination. In addition, the screening does not show high sensitivity, frequently presenting a false negativity because of immunosuppression caused by drugs used for arthritis. Recently, interferon-γ release assays (IGRA) have been used to screen LTBI with more sensitivity and specificity before treatment with anti-tumor necrosis factor-α drugs. Moreover, in our experience, IGRA are potentially useful in monitoring LTBI during biologic therapy.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".