Genetic diagnostic profiling in axial spondyloarthritis: a real world study.
Bibliographic record
Abstract
OBJECTIVES: Spondyloarthritis (SpA) is often diagnosed late in the course of the disease and improved methods for early diagnosis are required. We have tested the ability of genetic profiling to diagnose axial SpA (axSpA) as a whole group, or ankylosing spondylitis (AS) alone, in a cohort of chronic back pain patients. METHODS: 282 patients were recruited from centres in the United Kingdom, Germany, Taiwan, Canada, Columbia and Turkey as part of the ASAS classification criteria for axSpA study (ASAS cohort). Subjects were classified according to the ASAS axSpA criteria, and the modified New York Criteria for AS. Patients were genotyped for ~200,000 immune-mediated disease SNPs using the Illumina Immunochip. RESULTS: We first established the predictive accuracy of genetic data comparing 9,638 healthy controls and 4,428 AS cases from the homogenous International Genetics of AS (IGAS) Consortium Immunochip study which showed excellent predictive power (AUC=0.91). Genetic risk scores had lower predictive power (AUC=0.83) comparing ASAS cohort axSpA cases meeting the ASAS imaging criteria with IGAS controls. Comparing genetic risk scores showed moderate discriminatory capacity between IGAS AS and ASAS imaging positive cases (AUC 0.67±0.05), indicating that significant differences in genetic makeup exist between the cohorts. CONCLUSIONS: In a clinical setting of referred back pain patients suspected to have axial SpA we were unable to use genetic data to construct a predictive model better than that based on existing clinical data. Potential confounding factors include significant heterogeneity in the ASAS cohort, possibly reflecting the disease heterogeneity of axSpA, or differences between centres in ascertainment or classification performance.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".