Effect of Online Training on the Reliability of Assessing Sacroiliac Joint Radiographs in Axial Spondyloarthritis: A Randomized, Controlled Study
Bibliographic record
Abstract
Objective Radiographic assessment of sacroiliac joints (SIJs) according to the modified New York (mNY) criteria is key in the classification of axial spondyloarthritis but has moderate interreader agreement. We aimed to investigate the improvements of the reliability in scoring SIJ radiographs after applying an online real-time iterative calibration (RETIC) module, in addition to a slideshow and video alone. Methods Nineteen readers, randomized to 2 groups (A or B), completed 3 calibration steps: (1) review of manuscripts, (2) review of slideshow and video with group A completing RETIC, and (3) re-review of slideshow and video with group B completing RETIC. The RETIC module gave instant feedback on readers’ gradings and continued until predefined reliability ( ) targets for mNY positivity/negativity were met. Each step was followed by scoring different batches of 25 radiographs (exercises I to III). Agreement ( ) with an expert radiologist was assessed for mNY positivity/negativity and individual lesions. Improvements by training strategies were tested by linear mixed models. Results In exercises I, II, and III, mNY were 0.61, 0.76, and 0.84, respectively, in group A; and 0.70, 0.68, and 0.86, respectively, in group B (ie, increasing, mainly after RETIC completion). Improvements were observed for grading both mNY positivity/negativity and individual pathologies, both in experienced and, particularly, inexperienced readers. Completion of the RETIC module in addition to the slideshow and video caused a significant increase of 0.17 (95% CI 0.07-0.27;P= 0.002) for mNY-positive and mNY-negative grading, whereas completion of the slideshow and video alone did not ( = 0.00, 95% CI −0.10 to 0.10;P= 0.99). Conclusion Agreement on scoring radiographs according to the mNY criteria significantly improved when adding an online RETIC module, but not by slideshow and video alone.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".