An Examination of Work Instability, Functional Impairment, and Disease Activity in Employed Patients with Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: To evaluate the relationship between the Disease Activity Score 28-joint count (DAS28), Health Assessment Questionnaire (HAQ), and Rheumatoid Arthritis-Work Instability Scale (RAWIS); and to define thresholds for clinical assessments associated with moderate to high RA-WIS. METHODS: Employed patients with RA were evaluated using DAS28, HAQ, and RA-WIS during routine clinics. Relationships between these assessments were evaluated by simple correlation. Multiple linear regression modeling was performed using RA-WIS as an outcome variable and HAQ, DAS28, age, sex, occupation, and disease duration as input variables. Receiver-operating characteristic curves were then formulated to determine optimal DAS28, and HAQ cutoff points for RA-WIS >or= 10, along with the odds ratio (OR). RESULTS: Ninety patients with RA completed the RA-WIS, which was moderately correlated with DAS28 (r =0.53) and HAQ (r = 0.66). Fifty-four percent of RA-WIS was explained by DAS28 (p = 0.002), HAQ (p = 0.001), and sex (p = 0.04). A DAS28 of 3.81 and HAQ of 0.55 were clinically important thresholds. High DAS28 and HAQ were associated with high RA-WIS (OR(DAS) 14.17, OR(HAQ) 25.13, OR(DAS+HAQ) 29.9). CONCLUSION: Functional impairment and disease activity significantly and independently contributed to patient-perceived work instability risk.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".