Central reader evaluation of MRI scans of the sacroiliac joints from the ASAS classification cohort: discrepancies with local readers and impact on the performance of the ASAS criteria
Bibliographic record
Abstract
OBJECTIVES: The Assessment of SpondyloArthritis international Society (ASAS) MRI working group conducted a multireader exercise on MRI scans from the ASAS classification cohort to assess the spectrum and evolution of lesions in the sacroiliac joint and impact of discrepancies with local readers on numbers of patients classified as axial spondyloarthritis (axSpA). METHODS: Seven readers assessed baseline scans from 278 cases and 8 readers assessed baseline and follow-up scans from 107 cases. Agreement for detection of MRI lesions between central and local readers was assessed descriptively and by the kappa statistic. We calculated the number of patients classified as axSpA by the ASAS criteria after replacing local detection of active lesions by central readers and replacing local reader radiographic sacroiliitis by central reader structural lesions on MRI. RESULTS: Structural lesions, especially erosions, were as frequent as active lesions (≈40%), the majority of patients having both types of lesions. The ASAS definitions for active MRI lesion typical of axSpA and erosion were comparatively discriminatory between axSpA and non-axSpA. Local reader overcall for active MRI lesions was about 30% but this had a minor impact on the number of patients (6.4%) classified as axSpA. Substitution of radiography with MRI structural lesions also had little impact on classification status (1.4%). CONCLUSION: Despite substantial discrepancy between central and local readers in interpretation of both types of MRI lesion, this had a minor impact on the numbers of patients classified as axSpA supporting the robustness of the ASAS criteria for differences in assessment of imaging.
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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 | 0.024 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".