Advancing Early Identification of Axial Spondyloarthritis: An Interobserver Comparison of Extended Role Practitioners and Rheumatologists
Bibliographic record
Abstract
OBJECTIVE: To compare clinical impression and confidence of extended role practitioners (ERP) with those of rheumatologists experienced in axial spondyloarthritis (axSpA) according to (1) evaluation of patients with chronic back pain assessed for axSpA; and (2) magnetic resonance imaging (MRI) recommendation for further investigation of these patients. METHODS: Patients with ≥ 3 months of back pain and age of onset < 45 years were referred for axSpA evaluation. An ERP assessed consecutive patients and recorded standardized clinical information in written form. Three rheumatologists subsequently evaluated each patient based on the recorded information. Patients were classified as having axSpA or mechanical back pain based on clinical and investigative findings. Level of confidence was noted for classification and MRI indication. Agreement between assessors was evaluated using percentage agreement and κ coefficient. RESULTS: Fifty-seven patients were assessed. Interobserver agreement of clinical impression for all raters was moderate (κ = 0.52). Agreement of clinical impression between ERP and rheumatologists ranged between 71.2% (κ = 0.41) and 79.7% (κ = 0.57). Agreement of clinical impression among rheumatologists ranged from 74.1% (κ = 0.49) to 79.7% (κ = 0.58). All rater agreement for MRI indication was fair (κ = 0.37). ERP agreement with rheumatologist for MRI recommendation ranged from 64.2% (κ = 0.32) to 75% (κ = 0.48). Agreement for MRI indication among rheumatologists ranged from 62.9% (κ = 0.27) to 74% (κ = 0.47). Confidence in clinical impression was similar among all practitioners. CONCLUSION: ERP with specialty training in inflammatory arthritis demonstrate clinical impressions comparable with those of rheumatologists in the assessment of axSpA. Incorporation of such roles into existing models of care may assist in early detection of axSpA.
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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 | 0.035 | 0.098 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".