Diagnosis of Latent Tuberculosis and Prevention of Reactivation in Rheumatic Patients Receiving Biologic Therapy: International Recommendations
Bibliographic record
Abstract
OBJECTIVE: To review the official international recommendations on the management of latent tuberculosis infection (LTBI) in patients with rheumatic diseases undergoing biologic therapy. METHODS: A systematic search of all clinical practice recommendations on the diagnosis and treatment of LTBI in rheumatic patients eligible for starting biologic drugs published between January 2002 and March 2013. RESULTS: For the diagnosis of LTBI, based on positivity of tuberculin skin test (TST), interferon-γ release assay (IGRA) is also available. Most recommendations advise using both TST and IGRA, especially in case of Bacillus Calmette-Guérin vaccination, to screen patients before commencing biologic drugs. There is a general consensus that evaluation of the global risk of TB infection is a crucial point and that patients with LTBI must receive chemoprophylaxis prior to biologic therapy. However, recommendations on the need for rescreening for activation of LTBI or new TB infection while patients are being treated are inadequate. Nevertheless, the main concern is poor compliance with TB recommendations of rheumatologists in clinical practice, which seems to be the main cause of the occurrence of active TB in rheumatic patients receiving biologic therapy. CONCLUSION: Notwithstanding some differences, mainly related to regional TB incidence, international recommendations strongly suggest careful screening for LTBI before starting biologic therapy. However, the critical point is implementing dissemination and awareness of the recommendations among rheumatologists to improve adherence in real life.
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 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 | 0.009 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".