Validation of SPARCC MRI-RETIC e-tools for increasing scoring proficiency of MRI sacroiliac joint lesions in axial spondyloarthritis
Bibliographic record
Abstract
BACKGROUND: The Spondyloarthritis Research Consortium of Canada (SPARCC) developers have created web-based calibration modules for the SPARCC MRI sacroiliac joint (SIJ) scoring methods. We aimed to test the impact of applying these e-modules on the feasibility and reliability of these methods. METHODS: e-modules contain cases with baseline and follow-up scans and an online scoring interface. Visual real-time feedback regarding concordance/discordance of scoring with expert readers is provided by a colour-coding scheme. Reliability is assessed in real time by intraclass correlation coefficient (ICC), cases being scored until ICC targets are attained. Participating readers (n=17) from the EuroSpA Imaging project were randomised to one of two reader calibration strategies that each comprised three stages. Baseline and follow-up scans from 25 cases were scored after each stage was completed. Reliability was compared with a SPARCC developer, and the System Usability Scale (SUS) assessed feasibility. RESULTS: The reliability of readers for scoring bone marrow oedema was high after the first stage of calibration, and only minor improvement was noted following the use of the inflammation module. Greater enhancement of reader reliability was evident after the use of the structural module and was most consistently evident for the scoring of erosion (ICC status/change: stage 1 (0.42/0.20) to stage 3 (0.50/0.38)) and backfill (ICC status/change: stage 1 (0.51/0.19) to stage 3 (0.69/0.41)). The feasibility of both e-modules was evident by high SUS scores. CONCLUSION: e-modules are feasible, effective knowledge transfer tools, and their use is recommended before using the SPARCC methods for clinical research and tria.
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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 | 0.069 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".