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
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".