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Record W4387846001 · doi:10.1186/s13256-023-04185-5

Concurrence of familial Mediterranean fever and Behçet’s disease: a case report and review of the literature

2023· review· en· W4387846001 on OpenAlexaffabout
Adhora Mir, Catherine Ivory, Juthaporn Cowan

Bibliographic record

VenueJournal of Medical Case Reports · 2023
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammasome and immune disorders
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsFamilial Mediterranean feverMedicineBehcet's diseaseMEFVColchicineDiseaseDermatologyPrednisonePyrin domainImmunologyPediatricsInternal medicineGene mutationInflammationMutationGenetics

Abstract

fetched live from OpenAlex

BACKGROUND: Familial Mediterranean fever and Behçet's disease are distinct disorders that are prevalent in the Mediterranean and Middle Eastern populations. They are characterized by unprovoked inflammatory episodes caused by overexpression of proinflammatory cytokines. Although reported previously, the overlapping presentation of familial Mediterranean fever and Behçet's disease remains uncommon. CASE PRESENTATION: A 46-year-old Lebanese-Canadian man who presented with recurrent oral and genital ulcers, polyarticular synovitis, ocular swelling, recurrent infections, and fevers was later found to have heterozygous mutations of pathogenic MEFV c.2080A > G (p. Met 694Val) and c.2082G > A (p.Met694IIe) genes indicating familial Mediterranean fever. He was treated with prednisone, colchicine, and azathioprine, with inadequate symptoms control. Treatment was complicated by recurrent infections. CONCLUSIONS: Our case contributes to the growing literature demonstrating the presentation of predominantly Behçet's disease-like features in the setting of diagnosis of familial Mediterranean fever. These findings emphasize that clinicians should be aware that patients with familial Mediterranean fever may present with Behçet's disease-like clinical manifestations.

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)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.308
Threshold uncertainty score0.594

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.026
GPT teacher head0.343
Teacher spread0.317 · 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 designCase report
Domainnot available
GenreReview

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

Citations4
Published2023
Admission routes2
Has abstractyes

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