Management of Monogenic IL-1 Mediated Autoinflammatory Diseases in Childhood
Bibliographic record
Abstract
Monogenic Interleukin 1 (IL-1) mediated autoinflammatory diseases (AID) are rare, often severe illnesses of the innate immune system associated with constitutively increased secretion of pro-inflammatory cytokines. Clinical characteristics include recurrent fevers, inflammation of joints, skin, and serous membranes. CNS and eye inflammation can be seen. Characteristically, clinical symptoms are coupled with elevated inflammatory markers, such as C-reactive protein (CRP) and serum amyloid A (SAA). Typically, AID affect infants and children, but late-onset and atypical phenotypes are described. An in-depth understanding of autoinflammatory pathways and progress in molecular genetics has expanded the spectrum of AID. Increasing numbers of genetic variants with undetermined pathogenicity, somatic mosaicisms and phenotype variability make the diagnosis of AID challenging. AID should be diagnosed as early as possible to prevent organ damage. The diagnostic approach includes patient/family history, ethnicity, physical examination, specific functional testing and inflammatory markers (SAA, CRP) during, and in between flares. Genetic testing should be performed, when an AID is suspected. The selection of genetic tests is guided by clinical findings. Targeted and rapid treatment is crucial to reduce morbidity, mortality and psychosocial burden after an AID diagnosis. Management includes effective treat-to-target therapy and standardized, partnered monitoring of disease activity (e.g., AIDAI), organ damage (e.g., ADDI), patient/physician global assessment and health related quality of life. Optimal AID care in childhood mandates an interdisciplinary team approach. This review will summarize the current evidence of diagnosing and managing children with common monogenic IL-1 mediated AID.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".