Bibliographic record
Abstract
Tumor necrosis factor (TNF) is a proinflammatory cytokine involved in the pathogenesis of rheumatoid arthritis and other chronic inflammatory diseases. TNF also plays a critical role in host responses to intracellular pathogens and in granuloma formation and maintenance. The TNF-α inhibitors are a class of highly effective, targeted anti-inflammatory medications in widespread use for the treatment of rheumatoid arthritis. However, evidence has accumulated since the introduction of the TNF-antagonists that they are also associated with an increased risk of granulomatous infections, including tuberculosis and non-tuberculous mycobacterial (NTM) infections. This chapter will review the literature on the role of TNF in response to mycobacterial infection, the various TNF-α inhibitors and their biological differences, and the indications for TNF-α inhibitors in the treatment of rheumatoid arthritis. We will also review the literature to date on the risk of tuberculosis associated with rheumatoid arthritis and TNFantagonist therapy. We will discuss the clinical evaluation and treatment of latent tuberculosis infection in the setting of proposed TNF-antagonist therapy, as well as the presentation and treatment of active tuberculosis. We will review the current literature on the risk of NTM disease associated with TNF-α inhibitor therapy, as well as the presentation, treatment, and prevention of NTM disease in this setting.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.011 |
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".