Health-Related Quality of Life (HRQoL) in Idiopathic Inflammatory Myopathy: A Systematic Review
Bibliographic record
Abstract
Health-related quality of life (HRQoL) is a research priority in chronic diseases. We undertook a systematic review (registration #CRD42015024939) to identify, appraise and synthesize the evidence relating to HRQoL in idiopathic inflammatory myopathies (IIM). A comprehensive search was conducted in August 2015 using CINAHL, EMBase and Pubmed to identify studies reporting original data on HRQoL in IIM using generic HRQoL instruments. Characteristics of samples and results from selected studies were extracted and appraised using a standardized approach. Qualitative synthesis of the results was performed. Ten studies including a total of 654 IIM subjects were included in this systematic review. HRQoL was significantly impaired in all subsets of IIM compared with the general population. Disease activity, disease damage and chronic disease course were associated with poorer HRQoL. Insufficient or conflicting results were found in associations between clinical features, treatment, disease duration and mood or illness perception, and HRQoL in IIM. This study suggests that HRQoL is impaired in IIM. However, due to the paucity and heterogeneity of the evidence to date, robust estimates are lacking and significant knowledge gaps persist. There is a need for studies that systematically investigate the correlates and trajectory of HRQoL in IIM.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".