MétaCan
Menu
Back to cohort
Record W3213586222 · doi:10.1111/bcp.15149

Conventional and novel therapeutic options in children with familial Mediterranean fever: A rare autoinflammatory disease

2021· review· en· W3213586222 on OpenAlexaff
Dimitri Poddighe, Micol Romano, Facundo García‐Bournissen, Erkan Demirkaya

Bibliographic record

VenueBritish Journal of Clinical Pharmacology · 2021
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammasome and immune disorders
Canadian institutionsWestern University
Fundersnot available
KeywordsFamilial Mediterranean feverMedicineCanakinumabAnakinraColchicineDiseaseAmyloidosisIntensive care medicineArthritisPediatricsMEFVDermatologyImmunologyInternal medicineGene mutation

Abstract

fetched live from OpenAlex

Familial Mediterranean fever (FMF) is the most common monogenic autoinflammatory disease and is usually diagnosed in childhood, especially in the first decade of life. Paediatric FMF is characterized by a protean clinical expression and a variable therapeutic response, which can make its medical management very challenging. However, even if long-term complications of untreated FMF (e.g. amyloidosis and related organ damage) are less frequent in children compared to adults, they are not uncommon. Colchicine is the mainstay of the therapy in paediatric FMF; however, if children develop colchicine intolerance and/or resistance, biologics, particularly interleukin-1 antagonists, must be considered. Other conventional or biological therapeutic options do not currently have appropriate evidence-based support, except for some specific clinical presentations (e.g., arthritis). In this review, we discuss the biological basis and the clinical evidence for the current pharmacological treatment options available for paediatric FMF.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.045
GPT teacher head0.384
Teacher spread0.339 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations14
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueBritish Journal of Clinical PharmacologySame topicInflammasome and immune disordersFrench-language works237,207