Risk factors for severe Muckle‐Wells syndrome
Bibliographic record
Abstract
OBJECTIVE: Muckle-Wells syndrome (MWS) is an inherited autoinflammatory disease resulting in excessive interleukin-1 release. It is unknown whether demographic, clinical, or laboratory characteristics at the time of diagnosis may identify patients who are at high risk for severe disease activity. This study was undertaken to analyze clinical and laboratory features of MWS, compare genetically defined subcohorts, and identify risk factors for severe MWS. METHODS: A multicenter cohort study of consecutive MWS patients was performed. Parameters assessed included clinical features, MWS Disease Activity Score (MWS-DAS), inflammation markers, and cytokine levels. E311K mutation-positive patients were compared with E311K mutation-negative patients. Putative risk factors for severe MWS (defined as an MWS-DAS score of ≥10) were assessed in univariate analyses, and significant predictors were entered into a multivariate model. RESULTS: Thirty-two patients (15 male and 17 female) were studied. The most frequent organ manifestations were musculoskeletal symptoms and eye and skin disorders. Renal disease and hearing loss were seen in >50% of the patients. Genetically defined subcohorts had distinct phenotypes. Severe disease activity was documented in 19 patients (59%). Predictors of severe MWS identified at the time of diagnosis were female sex, hearing loss, musculoskeletal disease, increased erythrocyte sedimentation rate, and low hemoglobin level. Female sex and hearing loss remained significant after adjustment for age in a multivariate model (relative risk 1.8 and 2.6, respectively). CONCLUSION: MWS patients at high risk for severe disease can be identified at the time of diagnosis. Female patients presenting with hearing loss have the highest likelihood of manifesting severe MWS and should be considered a high-risk group.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".