Effect of Coping Strategies on Patient and Physician Perceptions of Disease Severity and Disability in Systemic Sclerosis
Bibliographic record
Abstract
Objective Systemic sclerosis (SSc) results in impaired function, disability, and reduced health-related quality of life. We investigated the effect of coping strategies on the patient global assessment of health (PtGA) and Health Assessment Questionnaire–Disability Index (HAQ-DI), after controlling for clinical characteristics and disease activity. We also explored the relationship between coping strategies and the correlation between the PtGA and physician global assessment (PGA) in SSc. Methods We undertook posthoc analyses using baseline data obtained from the Raynaud Symptom Study (RSS). The PtGA, Coping Strategies Questionnaire, Pain Catastrophizing Scale, and Scleroderma Health Assessment Questionnaire were collected alongside the PGA, clinical characteristics, and patient demographics. Multivariable linear regression models and correlations were used to evaluate the relationship between coping strategies with the PtGA, HAQ-DI, and PGA. Results Of the 107 patients with SSc enrolled in the RSS, there were sufficient data available for the analysis of 91 participants. The mean PtGA was 40/100 (SD 27) and the mean HAQ-DI was 0.87/3.0 (SD 0.73). After controlling for clinical and patient demographics, pain catastrophizing and maladaptive coping skills were significantly associated with the PtGA and HAQ-DI scores ( P < 0.05 for both), but not the PGA. Conclusion The effect of coping strategies on PtGA and HAQ-DI (but not PGA in SSc) could influence the result of composite measures incorporating these outcome measures. Interventions to improve patient coping skills may support increased resilience and improve patient-perceived functional status and PtGA in SSc.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".