Validation of SPARCC MRI-RETIC E-Tools for Increasing Scoring Proficiency of MRI Sacroiliac Joint Lesions in Axial Spondyloarthritis
Bibliographic record
Abstract
Abstract Background. The Spondyloarthritis Research Consortium of Canada (SPARCC) developers have created novel web-based calibration modules for the SPARCC MRI Sacroiliac Joint (SIJ) inflammation and structural scoring methods (SPARCC-SIJRETIC−INF, SPARCC-SIJRETIC−STR) based on DICOM images and real-time iterative feedback (RETIC). We aimed to test the impact of applying these modules on feasibility and inter-observer reliability (status/change) of the SPARCC SIJ methods. Methods The SPARCC-SIJRETIC modules each contain 50 DICOM axial spondyloarthritis (axSpA) cases with baseline and follow-up scans and an online scoring interface. Continuous visual real-time feedback regarding concordance/discordance of scoring per SIJ quadrant (bone marrow edema (BME), erosion, fat lesion) or halves (backfill, ankylosis) with expert readers is provided by a color-coding scheme. Reliability is assessed in real-time by intra-class correlation coefficient (ICC), cases being scored until ICC targets are attained. Participating readers (n = 17) from the EuroSpA Imaging project were randomized, stratified by reader expertise with SPARCC-SIJ, to one of two reader calibration strategies that each comprised 3 stages. Baseline and follow-up scans from 25 cases were scored using SPARCC-SIJ after each stage was completed; none of these 75 cases were included in the SPARCC-SIJRETIC modules. Reliability was compared to an expert radiologist (SPARCC developer), and the Systems Usability Scale (SUS) assessed feasibility. Results The reliability of EuroSpA readers for scoring BME was high (ICC status/change ≥ 0.80) even after the first stage of calibration, and only minor improvement was noted following the use of the SPARCC-SIJRETIC−INF module. Greater enhancement of reader reliability from stages 1 to 3 was evident after the use of the SPARCC-SIJRETIC−STR module, especially for inexperienced readers, 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 RETIC modules was evident by reading time per case of readers after calibration being comparable to SPARCC developers and by the high SUS scores reported by most readers. Conclusion The SPARCC-SIJRETIC modules are feasible, effective knowledge transfer tools for the SPARCC MRI SIJ scoring methods. They are recommended for routine calibration of readers before using these methods for clinical research and trials.
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 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 | 0.060 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".