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Record W3024702749 · doi:10.1007/s00330-020-06807-8

Imaging assessment of children presenting with suspected or known juvenile idiopathic arthritis: ESSR-ESPR points to consider

2020· review· en· W3024702749 on OpenAlexaff
Robert Hemke, Nele Herregods, Jacob L. Jaremko, Gunnar Åström, Derk Avenarius, Fabio Becce, Dennis K. Bielecki, Mikael Boesen, Danoob Dalili, Chiara Giraudo, Kay‐Geert Hermann, Paul Humphries, Amanda Isaac, Anne Grethe Jurik, Andrea Klauser, Ola Kvist, Frederiek Laloo, Mario Maas, Ádám R. Mester, Edwin H. G. Oei, Amaka C Offiah, Patrick Omoumi, Olympia Papakonstantinou, Athena Plagou, Susan C. Shelmerdine, Paolo Simoni, Iwona Sudoł‐Szopińska, Laura Tanturri de Horatio, James Teh, Lennart Jans, Karen Rosendahl

Bibliographic record

VenueEuropean Radiology · 2020
Typereview
Languageen
FieldMedicine
TopicAutoimmune and Inflammatory Disorders Research
Canadian institutionsUniversity of Alberta
FundersMedical Research CouncilAmsterdam University Medical Centers
KeywordsMedicineArthritisNeuroradiologyInterventional radiologyInflammatory arthritisRadiologyPhysical therapyInternal medicine

Abstract

fetched live from OpenAlex

Juvenile idiopathic arthritis (JIA) is the most common paediatric rheumatic disease. It represents a group of heterogenous inflammatory disorders with unknown origin and is a diagnosis of exclusion in which imaging plays an important role. JIA is defined as arthritis of one or more joints that begins before the age of 16 years, persists for more than 6 weeks and is of unknown aetiology and pathophysiology. The clinical goal is early suppression of inflammation to prevent irreversible joint damage which has shifted the emphasis from detecting established joint damage to proactively detecting inflammatory change. This drives the need for imaging techniques that are more sensitive than conventional radiography in the evaluation of inflammatory processes as well as early osteochondral change. Physical examination has limited reliability, even if performed by an experienced clinician, emphasising the importance of imaging to aid in clinical decision-making. On behalf of the European Society of Musculoskeletal Radiology (ESSR) arthritis subcommittee and the European Society of Paediatric Radiology (ESPR) musculoskeletal imaging taskforce, based on literature review and/or expert opinion, we discuss paediatric-specific imaging characteristics of the most commonly involved, in literature best documented and clinically important joints in JIA, namely the temporomandibular joints (TMJs), spine, sacroiliac (SI) joints, wrists, hips and knees, followed by a clinically applicable point to consider for each joint. We will also touch upon controversies in the current literature that remain to be resolved with ongoing research. KEY POINTS: • Juvenile idiopathic arthritis (JIA) is the most common chronic paediatric rheumatic disease and, in JIA imaging, is increasingly important to aid in clinical decision-making. • Conventional radiographs have a lower sensitivity and specificity for detection of disease activity and early destructive change, as compared to MRI or ultrasound. Nonetheless, radiography remains important, particularly in narrowing the differential diagnosis and evaluating growth disturbances. • Mainly in peripheral joints, ultrasound can be helpful for assessment of inflammation and guiding joint injections. In JIA, MRI is the most validated technique. MRI should be considered as the modality of choice to assess the axial skeleton or where the clinical presentation overlaps with JIA.

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)0.001
metaresearch head score (Gemma)0.005
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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.033
GPT teacher head0.343
Teacher spread0.310 · 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

Citations74
Published2020
Admission routes1
Has abstractyes

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