Delayed diagnosis of Muckle-Wells syndrome – analysis of influencing factors
Bibliographic record
Abstract
In two rheumatology centers a cohort of consecutive children and adults with genetically confirmed MWS were interviewed using a previously developed standardized questionnaire. The tool captures a total of 55 variables including patient related demographic factors, referral process related variables and presenting MWS symptoms at time of MWS diagnosis. A total of 32 patients, 18 females/14 males were included. These were 10 children and 22 adults with active MWS and confirmed mutations of the NLRP3 gene. The median age was 36 years (range 3-75). The median distance from home to the rheumatology center was 20km (range 7-577). The mean time elapsed between first consultation and final diagnosis was 21.9 years (range 0 – 63 years). The major symptoms reported by the patients were musculoskeletal (75%), skin disease (63%), eye disease (47%), relapsing fevers (41%) and hearing loss (34%). Diagnoses preceding the correct recognition of MWS included rheumatic disease (41%), conjunctivitis (41%), hearing loss (31%) and urticaria (28%). No definite diagnosis was made in 25%. Medical subspecialties most frequently consulted first were pediatricians (38%) and general practitioners (31%). In 84% the physician to establish the diagnosis of MWS was a rheumatologist. In patients with MWS, diagnosis is dramatically delayed most likely due to low level of knowledge regarding the disease. As severe disease sequelae like sensorineural deafness and amyloidosis may be prevented by early diagnosis and effective IL-1 inhibition the education of medical professionals in the area of autoinflammatory diseases needs to be intensified. Designated reference centers may contribute in accelerating the process of diagnosis and 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".