Development and Preliminary Validation of the Spondyloarthritis Research Consortium of Canada Magnetic Resonance Imaging Sacroiliac Joint Structural Score
Bibliographic record
Abstract
OBJECTIVE: There is an unmet need for reliable assessment of structural progression in the sacroiliac joints (SIJ) of patients with spondyloarthritis (SpA), but radiography is unreliable and lacks responsiveness. We aimed to develop and validate a new scoring method for structural lesions based on magnetic resonance imaging (MRI), the Spondyloarthritis Research Consortium of Canada (SPARCC) SIJ Structural Score (SSS). METHODS: The SSS method for assessment of structural lesions is based on T1-weighted spin echo MRI, validated lesion definitions, slice selection according to well-defined anatomical principles, and dichotomous scoring (lesion present/absent) of 5 consecutive slices through the cartilaginous portion of the joint. Scoring ranges are fat metaplasia (0-40), erosion (0-40), backfill (0-20), and ankylosis (0-20). We progressively conducted 3 validation exercises with 2-4 readers on baseline, and either 2-year (exercises 1 and 2) or 1-year (exercise 3) scans from 147 patients with SpA assessed blinded to timepoint. Interobserver reliability was assessed by intraclass correlation coefficient (ICC) and smallest detectable change (SDC). RESULTS: Interobserver reliability for status score was good to excellent for ankylosis (ICC 0.79-0.98), consistently good for fat metaplasia (ICC 0.71-0.78), moderate to good for erosion (ICC 0.58-0.62), and fair to good for backfill (ICC 0.35-0.66). Reliability for change scores was moderate to good for all structural lesions despite the relatively small changes in scores, and was highest for fat metaplasia when both ICC and SDC values were compared. CONCLUSION: The new SPARCC MRI SSS method can detect structural changes in the SIJ with acceptable reliability over a 1-2-year timeframe, and should be further validated in patients with SpA.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".