Mood, Disability, and Quality of Life among a Subgroup of Rheumatoid Arthritis Individuals with Experiential Avoidance and Anxiety Sensitivity
Bibliographic record
Abstract
Objective. The current study aimed to identify and characterize distinct RA subgroups based on their level of EA and AS and compares the difference among the subgroups in mood, disability, and quality of life. Methods. Individuals with chronic pain for at least 3 months were recruited from an academic rheumatoid clinic. Participants were assessed for demographic, psychosocial, and personality measures. A two-step cluster analysis was conducted to identify distinct subgroups of patients. Differences in clinical outcomes were compared using the Multivariate ANOVA based on cluster membership. Results. From a total of 223 participants, three distinct subgroups were formed based on cluster analysis. Cluster 1 (N = 78) included those with low levels of both EA and AS. Cluster 2 (N = 81) consisted of individuals with moderate levels of EA and low levels AS. Cluster 3 (N = 64) included those with moderate levels of EA and high AS. Compared to those in Cluster 1, those in Cluster 3 had significantly higher levels of mood impairment and disability and lower quality of life (p < 0.05). Significantly lower levels of mood impairment were seen in Cluster 1 compared to Cluster 2 (p < 0.05). However, no significant difference in disability or quality of life was seen between the two groups. Conclusions. The three subgroups differed significantly in levels of impairment in mood, disability, and quality of life. However, levels of EA had a greater impact on disability and quality of life than AS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 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".