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Record W2759585016 · doi:10.3899/jrheum.161583

Development and Validation of MRI Sacroiliac Joint Scoring Methods for the Semiaxial Scan Plane Corresponding to the Berlin and SPARCC MRI Scoring Methods, and of a New Global MRI Sacroiliac Joint Method

2017· article· en· W2759585016 on OpenAlexvenueaboutno aff
Pernille Hededal, Mikkel Østergaard, Inge Juul Sørensen, Anne Gitte Loft, Jens Skøt Hindrup, Gorm Thamsborg, Karsten Asmussen, Oliver Hendricks, Jesper Nørregaard, Jakob Møllenbach Møller, Anne Grethe Jurik, Lone Morsel, Lone Balding, Susanne Juhl Pedersen

Bibliographic record

VenueThe Journal of Rheumatology · 2017
Typearticle
Languageen
FieldMedicine
TopicSpine and Intervertebral Disc Pathology
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineSacroiliac jointJoint (building)Nuclear medicineMagnetic resonance imagingRadiology

Abstract

fetched live from OpenAlex

Objective. To develop semiaxial magnetic resonance imaging (MRI) scoring methods for assessment of sacroiliac joint (SIJ) bone marrow edema (BME) in patients with axial spondyloarthritis, and to compare the reliability with equivalent semicoronal scoring methods. Methods. Two semiaxial SIJ MRI scoring methods were developed based on the principles of the semicoronal Berlin and Spondyloarthritis Research Consortium of Canada (SPARCC) methods. A global quadrant-based method was also developed. Baseline and 12-week MRI of the SIJ from 51 patients participating in a randomized double-blind placebo-controlled trial of adalimumab 40 mg every other week versus placebo were scored by the semiaxial and the corresponding semicoronal methods. Results were compared by linear regression analysis. The reproducibility and sensitivity were evaluated by intraclass correlation coefficients (ICC) and smallest detectable change [SDC, absolute values and percentage of the highest observed score (SDC-HOS)]. Results. Interreader and intrareader ICC were moderate to very high for semiaxial scoring methods (baseline 0.83–0.88 and 0.85–0.97; change 0.33–0.78), while high to very high for semicoronal scoring methods (baseline 0.90–0.92 and 0.93–0.97; change 0.77–0.89). Association between semiaxial and semicoronal scores were high for both the Berlin and SPARCC method (baseline: R2= 0.93 and 0.88; change: R2= 0.82 and 0.87, respectively), while lower for the global method (baseline: R2= 0.79; change: R2= 0.54). The SDC-HOS were 9.8–18.6% and 5.9–10.7% for the semiaxial and semicoronal methods, respectively. Conclusion. Detection of SIJ BME in the semiaxial scan plane is feasible and reproducible. However, a slightly lower reliability of all 3 semiaxial methods supports the general practice of using the coronal scan-plane in therapeutic studies.

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)0.018
metaresearch head score (Gemma)0.030
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: Methods · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.097

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.030
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.074
GPT teacher head0.418
Teacher spread0.344 · 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
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

Citations24
Published2017
Admission routes2
Has abstractyes

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