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Record W2014873383 · doi:10.1186/1546-0096-10-s1-a88

Delayed diagnosis of Muckle-Wells syndrome – analysis of influencing factors

2012· article· en· W2014873383 on OpenAlexaff
Jasmin Kuemmerle‐Deschner, Samuel Dembi Samba, Isabelle Koné‐Paut, Isabelle Marié, Katharina Gramlich, Sandra Hansmann, Theodoros Xenitidis, Susanne M. Benseler

Bibliographic record

VenuePediatric Rheumatology · 2012
Typearticle
Languageen
FieldMedicine
TopicEosinophilic Disorders and Syndromes
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsMedicineDermatologyRashRheumatologyDiseaseInternal medicineAmyloidosisPediatricsIntensive care medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.695

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.015
GPT teacher head0.265
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2012
Admission routes1
Has abstractyes

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