Classification and Diagnosis of Axial Spondyloarthritis — What Is the Clinically Relevant Difference?
Bibliographic record
Abstract
OBJECTIVE: The Assessment of Spondyloarthritis international Society (ASAS) classification criteria for axial spondyloarthritis (axSpA) have added nonradiographic axSpA (nr-axSpA) to the classic ankylosing spondylitis (AS) as defined by the modified New York criteria. However, some confusion remains about differences between classification and diagnosis of axSpA. Our objective was to analyze differences between classification and diagnostic criteria by discussing each feature of the classification criteria based on real cases. METHODS: The clinical features of the ASAS classification criteria were evaluated in relation to their significance for an expert diagnosis of axSpA. Twenty cases referred to our tertiary center outpatient clinic were selected because of an incorrect diagnosis of axSpA: 10 cases in which axSpA had been excluded initially because the classification criteria were not fulfilled, and 10 patients who had been previously diagnosed with axSpA because the classification criteria were fulfilled. Upon reevaluation, the former were diagnosed with axSpA while the latter had other diseases. RESULTS: All items that are part of the classification criteria show some variability related to their relevance for a diagnosis of axSpA. There are clinical features suggestive of axSpA that are not part of the classification criteria. Misinterpretation of imaging procedures contributed to false-positive results. Rarely, other diseases may mimic axSpA. CONCLUSION: Because the sensitivity and specificity of the axSpA classification criteria have been around 80% in clinical trials, some false-positive and false-negative cases were expected. It is hoped that their detailed description and discussion will help to increase the understanding of diagnosing axSpA in relation to the ASAS classification criteria.
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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 | 0.025 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".