Restarting Biologics and Management of Patients with Flares of Inflammatory Rheumatic Disorders or Psoriasis During Active Tuberculosis Treatment
Bibliographic record
Abstract
Our aim was to review the evidence concerning optimal timing for restarting biologics in patients with active tuberculosis (TB), and the management of relapsing rheumatoid arthritis (RA), psoriatic arthritis (PsA), ankylosing spondylitis (AS), and psoriasis during treatment for TB. Few or no indications are available for 2 important challenges for clinicians: the timing for restarting biologics in patients with TB reactivation and the management of the underlying disorder. In the absence of clear evidence, guidelines and experts suggest restarting anti-tumor necrosis factor-α (TNF-α) agents after completion of an active TB therapy course, but no indications are available on the appropriate management of patients with flares of underlying rheumatic disease or psoriasis. Among anti-TNF-α agents, etanercept is associated with the lowest risk of TB reactivation, and non-anti-TNF-α biologics and several nonbiologic drugs are associated with low/no risk of TB reactivation. Therefore, for patients with relapsing RA, PsA, AS, or psoriasis during TB treatment we propose a therapeutic schedule modulated by disease activity and individual single drug-related TB risk.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".