Identifying Target Areas of Treatment for Depressed Early Inflammatory Arthritis Patients
Bibliographic record
Abstract
BACKGROUND: The goal of this study was to identify target areas for psychosocial intervention for depressed patients with early inflammatory arthritis. METHODS: One hundred and sixty-five patients with early inflammatory arthritis (> or =1 joint with synovitis for > or =6 weeks and <1 year with a diagnosis of either rheumatoid or undifferentiated inflammatory arthritis) were referred to the McGill Early Arthritis Registry (McEAR) by their rheumatologist. McEAR patients agree to periodic physical exams and to completing questionnaires. Demographic, disease and psychosocial factors were compared between patients screening positive and negative for depression using independent samples t tests and Pearson's chi(2) test and then were entered into a logistic regression model examining the likelihood of screening positive for depression. RESULTS: Thirty-eight (23%) patients screened positive for depressive symptoms. Patients with symptoms of depression had significantly worse disease severity, disability, and pain, engaged in more emotional preoccupation coping, had less self-efficacy for pain and other arthritis-related symptoms, smaller social networks and were less satisfied with social support than the nondepressed group. In logistic regression analyses, pain and emotional preoccupation coping were positively related to the likelihood of screening positive for depression, while satisfaction with social support was negatively related to the likelihood of screening positive for depression CONCLUSION: Higher pain levels, emotional preoccupation coping and dissatisfaction with social support were related to depressive symptoms in this study. This suggests that the optimal care of depressed patients with inflammatory arthritis would include a psychosocial approach that addresses these specific target areas.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".