Dispositional Affect in Unique Subgroups of Patients with Rheumatoid Arthritis
Bibliographic record
Abstract
Background. Patients with rheumatoid arthritis may experience increased negative outcomes if they exhibit specific patterns of dispositional affect. Objective. To identify subgroups of patients with rheumatoid arthritis based on dispositional affect. The secondary objective was to compare mood, pain catastrophizing, fear of pain, disability, and quality of life between subgroups. Methods. Outpatients from a rheumatology clinic were categorized into subgroups by a cluster analysis based on dispositional affect. Differences in outcomes were compared between clusters through multivariate analysis of covariance. Results. 227 patients were divided into two subgroups. Cluster 1 (n = 85) included patients reporting significantly higher scores on all dispositional variables (experiential avoidance, anxiety sensitivity, worry, fear of pain, and perfectionism; all p < 0.001) compared to patients in Cluster 2 (n = 142). Patients in Cluster 1 also reported significantly greater mood impairment, pain anxiety sensitivity, and pain catastrophizing (all p < 0.001). Clusters did not differ on quality of life or disability. Conclusions. The present study identifies a subgroup of rheumatoid arthritis patients who score significantly higher on dispositional affect and report increased mood impairment, pain anxiety sensitivity, and pain catastrophizing. Considering dispositional affect within subgroups of patients with RA may help health professionals tailor interventions for the specific stressors that these patients experience.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".