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Record W7133029245

Development and Validation of the Juvenile Idiopathic Arthritis Magnetic Resonance Imaging - Sacroiliac Joint Scoring System (JAMRIS-SIJ)

2023· dissertation· W7133029245 on OpenAlexafffund
Tarimobo M. Otobo

Bibliographic record

VenueTSpace · 2023
Typedissertation
Language
FieldMedicine
TopicAutoimmune and Inflammatory Disorders Research
Canadian institutionsUniversity of Toronto
FundersHealth CanadaHospital for Sick Children
KeywordsMagnetic resonance imagingSacroiliac jointEnthesitisArthritisSacroiliitisSynovitisScoring systemInflammatory arthritis
DOInot available

Abstract

fetched live from OpenAlex

ABSTRACTIntroduction: This thesis utilized a mixed method approach in the development and validation of the Juvenile Arthritis Magnetic Resonance Imaging Sacroiliac Joint Scoring System (JAMRIS-SIJ) in Juvenile Idiopathic Arthritis (JIA). JIA is a chronic inflammatory disease that affects joint function, resulting in a significant effect on the quality of life. MRI is the best diagnostic modality for SIJ imaging in JIA due to its ability to detect early signs of inflammation before radiography, providing for the direct visualization of osteochondral changes to guide treatment. The objective of this research thesis project was to develop a reliable and valid imaging outcome measure that can detect change during JIA interventions. This dissertation describes a series of studies reporting the development and validation process of the JAMRIS-SIJ. Methods: JAMRIS-SIJ development was conducted using a combination of the synthesis of existing related scoring systems in the literature and a multi-iteration nominal group process among over 30 multidisciplinary international imaging and clinical experts to arrive at consensus on the measurement domain and items for the assessment of SIJ inflammation and damage. Thereafter, using data-driven methodology, cases of patients with JIA were selected to test the reliability and validity of the JAMRIS-SIJ. Results: An all-inclusive definition of the SIJ MRI lesions for the inflammation in the order of their relative importance weights in the JAMRIS-SIJ are osteitis (25.7%), bone marrow edema (24.3%), inflammation in erosion cavity (16.9%), joint space enhancement (13.1%), joint space fluid (9.1%), capsulitis (7.3%) and enthesitis (4.6%). Likewise, for damage domain are ankylosis (41.3%), erosion (25.1%), backfill (13.9%), sclerosis (10.7%), and fat metaplasia lesion (9.1%). The intraclass correlation coefficient for inter-rater reliability was 0.77 and 0.60 for inflammation and damage domain respectively. Conclusion: This project reports the novel JAMRIS-SIJ scoring system and initial stages of validation of this scoring system. To our knowledge, this is a pioneer initiative towards the development of a sacroiliac joint MRI outcome measurement tool targeted on the pediatric population considering the unique imaging characteristics of growing bones, cartilage, and bone marrow. The understanding gained from this research may lead to the development of future guidelines for MR imaging outcomes in rheumatology clinical trials.

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)0.061
metaresearch head score (Gemma)0.092
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.061
Threshold uncertainty score0.325

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0610.092
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0040.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.024
GPT teacher head0.297
Teacher spread0.273 · 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 designObservational
Domainnot available
GenreMethods

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
Published2023
Admission routes2
Has abstractyes

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