Health-related quality of life and its predictive factors in patients with systemic lupus erythematosus in southwest Iran: a cross-sectional study
Bibliographic record
Abstract
Abstract Background Quality of life (QoL) is an important measure in health assessment. It is impacted by several factors in Systemic Lupus Erythematosus (SLE) patients which are not entirely clear. This aims of study was evaluation of factors affecting QoL in SLE patients.Methods This cross-sectional study was performed on 140 Iranian SLE patients of Hafiz Hospital. Questionnaire Lupus Quality of Life (LupusQoL) was used to evaluate the quality of life. The patients were evaluated with this questionnaire during 4 weeks in eight dimensions of health, emotional health, body image, pain, planning, intimate relationships and the burden of others. Predictive factors of LupusQoL were evaluated using multivariate linear regression.Results The total mean QoL Score was 65.5 ± 22.4.The highest score of SLE patients' quality of life is related to planning 78.36 ± 25.03 and the lowest score of QoL was related to emotional 54.70 ± 30.51.The results of multivariate analysis showed that duration of disease (β:-1.14, 95% CI:-1.6,-0.61, P:0.001), physical activity(β:-13.2, 95% CI:-20.8,-5.4, P:0.001), kidney involvement (β:-10.38, 95% CI:-17.61,-3.15, P:0.03) and skin involvement(β:-9.5, 95% CI:-18.1,-0.77, P:0.023) were significantly related to the total mean QOL score of SLE patients.Conclusion The findings confirmed that the SLE patients enjoy the middle QoL which can be due to the various factors of disease activity, different bodily systems involvement, and local, cultural, national, and racial aspects.
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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 | 0.002 |
| 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 | 0.001 |
| 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".