Tapering of biological treatment in autoinflammatory diseases: a scoping review
Bibliographic record
Abstract
Abstract Background Biological treatment and treat-to-target approaches guide the achievement of inactive disease and clinical remission in Autoinflammatory Diseases (AID). However, there is limited evidence addressing optimal tapering strategies and/or discontinuation of biological treatment in AID. This study evaluates available evidence of tapering biological treatment and explores key factors for successful tapering. Methods A systematic literature search was conducted in Embase, MEDLINE, Cochrane Database of Systematic Reviews and Cochrane Central Register of Controlled Trials using the OVID platform (1990-08/2020). Bibliographic search of relevant reviews was also performed. Studies/case series (n ≥ 5) in AID patients aged ≤ 18 years with biological treatment providing information on tapering/treatment discontinuation were included. After quality assessment aggregated data were extracted and synthesized. Tapering strategies were explored. Results A total of 6035 records were identified. Four papers were deemed high quality, all focused on systemic juvenile idiopathic arthritis (sJIA) (1 open-label randomized trial, 2 prospective, 1 retrospective observational study). Biological treatment included anakinra (n = 2), canakinumab (n = 1) and tocilizumab (n = 1). Strategies in anakinra tapering included alternate-day regimen. Canakinumab tapering was performed randomized for dose reduction or interval prolongation, whereas tocilizumab was tapered by interval prolongation. Key factors identified included early start of biological treatment and sustained inactive disease. Conclusion Tapering of biological treatment after sustained inactive disease should be considered. Guidance for optimal strategies is limited. Future studies may leverage therapeutic drug monitoring in combination with pharmacometric modelling to further enhance personalized “taper-to-target” strategies respecting individual patients and diseases aspects.
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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 | 0.015 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.019 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".