Challenges in Diagnosing Muckle‐Wells Syndrome: Identifying Two Distinct Phenotypes
Bibliographic record
Abstract
OBJECTIVE: The diagnosis of Muckle-Wells syndrome (MWS) remains challenging due to the clinical heterogeneity and lack of diagnostic criteria. The aims of this study were to describe key elements of the diagnostic evaluation process in MWS and compare identified variables between patients diagnosed in childhood and adulthood. METHODS: A cohort study of consecutive patients with a clinical and genetic diagnosis of MWS was conducted at 2 reference centers for autoinflammatory diseases. Demographic information, clinical presentation, access to care, and preclinical evaluation variables were captured. Presenting symptoms were compared between groups of patients diagnosed in childhood and adulthood. Prediction analysis explored variables associated with late diagnosis. Correspondence analysis identified clinical phenotypes. RESULTS: A total of 34 MWS patients were included (16 males, 18 females) and median age at diagnosis was 31.5 years (range 0.5-75 years). Patients diagnosed during childhood reported musculoskeletal symptoms (62%), rash (62%), fever (54%), and abdominal pain (31%). Those diagnosed as adults described musculoskeletal symptoms (86%), rash (67%), hearing loss (52%), and fatigue (29%). Hearing loss was associated with late diagnosis, while access-to-care variables were not predictive. Correspondence analysis identified distinct clinical phenotypes as follows: an "inflammatory phenotype" (most commonly seen in patients diagnosed in childhood and characterized by relapsing fever and abdominal pain), an intermediate phenotype, and an "organ-disease" phenotype in patients diagnosed during adulthood and characterized by fatigue and hearing loss. CONCLUSION: Distinct clinical phenotypes were identified in patients with MWS. These are closely related to age at diagnosis. The presence of these phenotypes has to be considered when developing diagnostic criteria for MWS.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".