Clinimetric properties of the Chinese version of the early inflammatory arthritis detection tool
Bibliographic record
Abstract
BACKGROUND: Timely rheumatologic referral and management are crucial for patients with potential inflammatory arthritis. To meet this need, tools such as the early inflammatory arthritis (EIA) detection tool was developed and has been evaluated in Western populations. The aims of this study were to translate the English version of the EIA detection tool to Chinese and to determine its clinimetric properties in Taiwanese patients. METHODS: Twenty controls and 111 patients with established diagnosis of osteoarthritis, rheumatoid arthritis, systemic autoimmune diseases, psoriatic arthritis, and ankylosing spondylitis were recruited from a regional hospital in south Taiwan. Multivariate logistic regression analysis was used to evaluate the independent and significant variables associated with diagnosis by rheumatologists. A prediction model was also developed for differentiating between patients with inflammatory arthritis and those with non-inflammatory arthritis musculoskeletal conditions. RESULTS: The Chinese version of the EIA detection tool showed acceptable internal consistency (KR-20 coefficient 0.78) and test-retest reliability (κ statistic ranged from 0.43 to 0.94). A prediction model consisting of three EIA detection tool items (joint pain, swelling in hands or wrists, and ever been told to have rheumatoid arthritis) and sex was able to differentiate inflammatory arthritis and non-inflammatory arthritis musculoskeletal conditions with a sensitivity of 0.84, a specificity of 0.86, a positive predictive value of 0.92, and a negative predictive value of 0.76. CONCLUSIONS: The Chinese version of the EIA detection tool showed good clinimetric properties in this study population and it can be used to differentiate Taiwanese patients with inflammatory arthritis and non-inflammatory arthritis musculoskeletal conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".