Classification Criteria for Axial Disease in Youth With Juvenile Spondyloarthritis
Bibliographic record
Abstract
OBJECTIVE: The goal was to develop and validate classification criteria for axial juvenile spondyloarthritis (SpA; AxJSpA). METHODS: This international initiative consisted of four phases: (1) item generation, (2) item reduction, (3) criteria development, and (4) validation of the AxJSpA criteria by an independent team of experts in an internationally representative validation cohort. RESULTS: These criteria are intended to be used on youth with a physician diagnosis of juvenile SpA and for whom axial disease is suspected. Item generation consisted of a systematic literature review and a free-listing exercise using input from international physicians, which collectively resulted in 108 items. After the item reduction exercise and expert panel input, 37 items remained for further consideration. The final AxJSpA criteria domains included the following: imaging of active inflammation, imaging of structural lesions, pain chronicity, pain pattern, pain location, stiffness, and genetics. The most heavily weighted domains were active inflammation and structural lesions on imaging. Imaging typical of sacroiliitis was deemed necessary, but not sufficient, to classify a youth with AxJSpA. The threshold for classification of AxJSpA was a score of ≥55 (out of 100). When tested in the validation data set, the final criteria had a specificity of 97.5% (95% confidence interval [CI] 91.4%-99.7%), sensitivity of 64.3% (95% CI 54.9%-73.1%), and area under the receiver operating characteristic curve of 0.81 (95% CI 0.76%-0.86%). CONCLUSION: The new AxJSpA classification criteria require an entry criterion and a physician diagnosis of juvenile SpA and include seven weighted domains. The AxJSpA classification criteria are validated and designed to identify participants for research studies.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".