The Patient Self-Administered Inflammatory Arthritis Detection Study
Bibliographic record
Abstract
OBJECTIVE: Early diagnosis and treat-to-target strategies improve outcomes for patients with inflammatory arthritis (IA). One approach for reducing diagnostic delay is using standardized patient-completed questionnaires to support referral decisions. This study evaluated the discriminatory referral performance of 2 validated questionnaires in newly referred rheumatology patients in British Columbia, Canada. METHODS: Patients completed the Early Inflammatory Arthritis Questionnaire (EIAQ) and Case Finding Axial Spondyloarthritis (CaFaSpA) questionnaire. Predictive scores for IA were calculated using existing algorithms and compared to the reference standard of their rheumatologist diagnosis. Discriminative performance was tested using the area under the receiver-operating characteristics curve (AUC), and diagnostic performance was tested using metrics, including sensitivity and specificity. Exploratory regression models were used to predict IA with different combinations of questionnaire questions. RESULTS: Of 92 participants, 30 (33%) had time-sensitive IA (TS-IA), 35 (38%) other IA, and 27 (29%) non-IA. Time from referral to rheumatologist visits for patients with TS-IA was 44 days (IQR 28-83), 69 (IQR 40-102) for "other IA," 65 (IQR 34-99) for "non-IA," and was longer for women (+ 9 days) and in nonmetropolitan areas (+ 16 days). Only 7 patients had axial spondyloarthritis, precluding discriminative analysis of the CaFaSpA. The EIAQ had an AUC of 0.59 (95% CI 0.49-0.68), sensitivity of 33% (95% CI 19-51%), and specificity of 84% (95% CI 73-91); alternate algorithms based on EIAQ and CaFaSpA questions delivered AUCs up to 0.80 (95% CI 0.68-0.90). CONCLUSION: The results support the utility and feasibility of routine collection of EIAQ and CaFaSpA questionnaires for discriminating patients with IA from those with non-IA.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".