Tuberculosis Screening Before and During Treatment with Tumor Necrosis Factor Antagonists: Something Old, Something New
Bibliographic record
Abstract
The increased risk of tuberculosis (TB) with tumor necrosis factor (TNF-α) antagonist treatment is well recognized1. The risk seems to parallel the background risk of TB. In Spain, where the incidence in the general population is 23/100,000, the incidence risk ratio of TB was 4.13 (95% CI 2.59–6.83) in patients with rheumatoid arthritis (RA) compared to the general population and 19.9 (95% CI 16.2–24.8) in patients with RA who were exposed to TNF-α antagonists compared to those who were not. In Sweden, where the incidence is 5/100,000, the incidence risk ratio was 2.0 (95% CI 1.2–3.4) in patients with RA and 4.0 (95% CI 1.3–12) in patients with RA using TNF-α antagonists1,2. In more than half of the patients, TB during TNF-α antagonist use is extrapulmonary and/or disseminated1. There seems to be a bimodal pattern with reactivation of latent TB early during the treatment course, with primary active TB usually developing later in the course. After the implementation of routine screening programs, with increased awareness of the clinical and radiological clues along with use of the tuberculin skin test (TST) and interferon-γ (IFN-γ) release assays (IGRA), a significant decrease in TB reactivation was observed among patients using TNF-α antagonists3. However, there is still no consensus on whether TST or IGRA should be preferred for screening in these patients, who are usually immunocompromised because of the character of their diseases and the immunosuppressives they use. In this issue of The Journal , Costantino, et al 4 report the results of TST and T-SPOT.TB assay in a large cohort of patients who are candidates for TNF-α antagonist therapy. The TST is a time-honored method of showing that one has been exposed to mycobacteria. Its potential shortcomings are giving false-positive results because of … Address correspondence to Dr. G. Hatemi, Associate Professor, Istanbul University, Cerrahpasa Medical School, Department of Internal Medicine, Division of Rheumatology, Istanbul 34300, Turkey. E-mail: gulenhatemi{at}yahoo.com
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".