The Work Instability Scale for rheumatoid arthritis predicts arthritis‐related work transitions within 12 months
Bibliographic record
Abstract
OBJECTIVE: Among people with arthritis, the need for work transitions may signal a risk for more adverse work outcomes in the future, such as permanent work loss. Our aim was to evaluate the ability of the Work Instability Scale for Rheumatoid Arthritis (RA-WIS) to predict arthritis-related work transitions within a 12-month period. METHODS: Workers with osteoarthritis or rheumatoid arthritis (n = 250) from 3 clinical sites participated in self-administered surveys that assessed the impact of health on employment at multiple time points over 12 months. Multivariable logistic regressions were conducted to assess the ability of the RA-WIS (range 0-23, where 23 = highest work instability) to predict 4 types of work transition: reductions in work hours, disability leaves of absence, changes in job/occupation, or temporary unemployment, assembled as a composite outcome. Covariates assessed include age, sex, education, marital status, income, pain intensity, disease duration, and the Health Assessment Questionnaire. Areas under the receiver operating characteristic curves (AUROCCs) were also assessed to further examine the predictive ability of the RA-WIS and to determine optimal cut points for predicting specific work transitions. RESULTS: After 12 months, 21.7% (n = 50 of 230) of the participants had indicated at least one arthritis-related work transition. Higher baseline RA-WIS was predictive of such an outcome (relative risk [RR] 1.05 [95% confidence interval (95% CI) 1.00-1.11]), particularly at >17 (RR 2.30 [95% CI 1.11-4.77]). The RA-WIS cut point of >13 was found to be most accurate for prediction (AUROCC 0.68 [95% CI 0.58-0.78]). CONCLUSION: The RA-WIS demonstrated the ability to predict arthritis-related work transitions within a short timeframe, and could be a promising measurement candidate for risk prognostication where work disability outcomes are of concern.
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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 | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.012 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.007 |
| 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 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".