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Record W4412822350 · doi:10.1186/s12969-025-01135-x

Preliminary validation of a web-based MRI scoring system for children with chronic nonbacterial osteomyelitis (ChRonic nonbacterial Osteomyelitis Magnetic Resonance Imaging Scoring: CROMRIS)

2025· article· en· W4412822350 on OpenAlexaff
Farzana Nuruzzaman, T. Shawn Sato, Jennifer Stimec, Ramesh S. Iyer, Andrew Carbert, Joel Paschke, Lauren Potts, Meinrad Beer, Johanna Monsalve, Anh‐Vu Ngo, Mahesh Thapa, Xiaoyue Zhang, Walter P. Maksymowych, Polly J. Ferguson, Yongdong Zhao

Bibliographic record

VenuePediatric Rheumatology · 2025
Typearticle
Languageen
FieldMedicine
TopicOrthopedic Infections and Treatments
Canadian institutionsUniversity of AlbertaResearch CanadaHospital for Sick Children
FundersUCB PharmaNational Institute of Arthritis and Musculoskeletal and Skin DiseasesCelgeneNational Institutes of HealthPfizerRheumatology Research FoundationEli Lilly and Company
KeywordsMedicineMagnetic resonance imagingRadiologySoft tissueOsteomyelitisUsabilityTorsoTrunkSurgeryComputer scienceAnatomy

Abstract

fetched live from OpenAlex

BACKGROUND: The ChRonic nonbacterial Osteomyelitis Magnetic Resonance Imaging Scoring (CROMRIS) tool was developed to assess specific characteristics of bone and soft tissue inflammation on MR images of patients with CNO; however, this tool was labor intensive to utilize. We aimed (1) to refine and adapt this scoring method, (2) to assess the usability of this web-based CROMRIS system among radiologists and (3) to evaluate the absolute agreement of the components and summary CROMRIS scores at each body site, and the interrater reliability. METHODS: We used a qualitative, user-centered design approach involving software developers, rheumatologists, radiologists, and a patient artist to adapt the paper-based scoring system to a web-based prototype that was further refined by monthly meetings between the group members. A clickable-schematic-based CROMRIS system was developed to include all body regions: head (skull/mandible), spine, torso (clavicle, sternum, and ribs), pelvis, hands, feet, arms, and legs. Readers scored individual bone units to indicate the presence of bone marrow hyperintensity on STIR images (score 0-1), soft tissue/periosteal hyperintensity of surrounding tissue (score 0-1), and bony expansion (score 0-1), and quantified the signal size of the CNO lesion (scores 1-3 defined as < 25%, 25-50%, or > 50% of the estimated volume, respectively). The sum of these parameters for lesions detected on fluid-sensitive sequences was the CROMIS Activity Index (maximum score 720). Feedback for usability was reported with descriptive content analysis and continuous variables as means and categorical variables as percentages. Interrater reliability was assessed by free-marginal kappa (k) statistics and the intraclass correlation coefficient (ICC). RESULTS: The mean system usability score increased from 64.5 (below average) to 75 (above average) after user feedback. Interrater reliability for the CROMRIS Activity Index was excellent for clavicle, tibia, cervical and lumbar spines (> 0.9) and good to moderate for the remainder of the body regions. The mean kappa of each category of bones was > 0.6 demonstrating substantial interrater reliability among radiologists for the bone sites most affected by CNO, namely the long bones and clavicle. CONCLUSION: The web-based CROMRIS portal developed was usable and showed substantial-moderate agreement in the total CROMRIS Activity Index total scores among experienced radiologists after self-guided learning of the atlas and video. This tool can potentially be used in future clinical trials after calibration.

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)0.064
metaresearch head score (Gemma)0.081
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score0.339

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0640.081
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.002
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 head0.004
GPT teacher head0.230
Teacher spread0.226 · 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 designBench or experimental
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

Citations5
Published2025
Admission routes1
Has abstractyes

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