Tuberculosis Reactivation Risk in Dermatology
Bibliographic record
Abstract
The treatment of some dermatological diseases, especially psoriasis, has been revolutionized by the advent of biologic therapies that target various immune cells or cytokines. However, biologic therapies may affect the risk of active tuberculosis (TB). We review the published safety data about TB risk reactivation for biologic agents used in dermatology. According to recent findings, psoriasis itself could represent an independent risk factor for TB; a high prevalence of TB was found in patients with psoriasis (18.0%), even after adjusting for age, work, and other characteristics. Latent TB infection was more common in patients with psoriasis (50%) than in those with inflammatory bowel disease (24.2%). Risk of TB reactivation was also influenced by the type of agent used. Several structural and functional differences among biologic drugs could account for differences in risk of granulomatous infection. Different kinetics of currently available tumor necrosis factor (TNF) antagonists, leading to different TNF bioavailability in granulomatous tissue, may explain differences in TB reactivation among patients treated with biologics. One could argue that etanercept should be the first choice of anti-TNF agent in populations at high risk of TB. Risk of TB reactivation during treatment with other biologics is not yet well defined.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".