Refining treat-to-target strategies in cryopyrin-associated periodic syndromes: the role of inflammatory markers
Bibliographic record
Abstract
OBJECTIVES: Cryopyrin-associated periodic syndromes (CAPS) encompasses a spectrum of IL-1 driven systemic diseases with dramatic individual and societal burden. The study aimed to identify parameters and instruments to refine real-life treat-to-target (T2T) strategies and control CAPS disease activity. METHODS: A single-centre, longitudinal study of consecutive children and adults diagnosed with CAPS and treated with anti-IL-1 therapy was performed. Demographics, clinical phenotype and NLRP3 gene variants in addition to serial inflammatory markers and physician and patient/parent global assessments (PGA/PPGA) were captured. Effectiveness of anti-IL-1 T2T strategies and factors associated with therapy escalation were determined. RESULTS: A total of 54 CAPS patients with 759 follow-up visits were included; 31/54 (57%) were children; the median follow-up was 108 months (12-620). The moderate CAPS phenotype was present in 89%; overall 59% had pathogenic/likely pathogenic NLRP3 variants. Therapy adjustments were documented in 50/759 visits including 35 therapy escalations and 15 reductions; 74% of the therapy escalation visits were for children. At time of visit, 63% showed moderate, 37% severe clinical disease activity. Inflammatory markers remained largely normal. Significant improvement was observed in both PGA/PPGA throughout the study (P < 0.01). At the last follow-up, 96% of patients achieved remission. CONCLUSION: Guidance for refining real-life T2T strategies in CAPS cohorts can be drawn from serial assessments of PGA and PPGA, reliably reflecting changes in disease activity. Individual parameters including age and NLRP3 gene variants are important predictors, while the sensitivity of inflammatory markers is limited due to the confounding anti-IL-1 therapy.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| 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 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".