Validation of a Self-administered Inflammatory Arthritis Detection Tool for Rheumatology Triage
Bibliographic record
Abstract
OBJECTIVE: The benefits of early intensive treatment of inflammatory arthritis (IA) are dependent on timely and accurate case identification. In our study, a scoring algorithm for a self-administered IA detection tool was developed and validated for the rheumatology triage clinical setting. METHODS: A total of 143 consecutive consenting adults, newly referred to 2 outpatient rheumatology practices, completed the tool. A scoring algorithm was derived from the best-fit logistic regression model using age, sex, and responses to the 12 tool items as candidate predictors of the rheumatologists' blinded classification of IA. Bootstrapping was used to internally validate and refine the model. RESULTS: The 30 IA cases were younger than the 113 non-cases (p < 0.0001) and included clinical diagnoses of early IA (n = 10), rheumatoid arthritis (n = 9), and spondyloarthropathies (n = 11). Non-cases included osteoarthritis (n = 46), pain syndromes (n = 19), systemic lupus erythematosus (n = 5), and miscellaneous, noninflammatory musculoskeletal complaints (n = 43). The best-fit model included younger age, male sex, "trouble making a fist," "morning stiffness," "ever told you have RA," and "psoriasis diagnosis." The overall predictive performance (standard error, SE) of the derivation model was 0.91 (0.03). Internal validation of the derivation model across 200 bootstrap samples resulted in a mean predictive performance (SE) of 0.904 (0.002). The refined tool had a mean predictive performance (SE) of 0.915 (0.002), a sensitivity of 0.855 (0.005), and specificity of 0.873 (0.003). CONCLUSION: A simple, self-administered tool was developed and internally validated for the sensitive and specific detection of IA in a rheumatology waiting list sample. The tool may be used to triage IA from rheumatology referrals.
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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 | 0.028 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".