Determinants of health-related quality of life in a multinational systemic sclerosis inception cohort.
Bibliographic record
Abstract
OBJECTIVES: Patients with Behçet's disease (BD) constantly complain of fatigue and many have problems with poor sleep. This ultimately has a major impact on all aspects of normal living. To attempt to understand this, Artificial Intelligence (AI) was used to identify potential biomarkers. These were alpha-melanocyte stimulating hormone (α-MSH), vasoactive intestinal peptide (VIP) and some inflammatory cytokines. We assessed the association of fatigue, quality of sleep and disease activity with circulating concentration of α-MSH, VIP and inflammatory cytokines. METHODS: There were 127 participants, 97 BD patients, and 30 healthy controls (HC). All completed the Multi-Dimensional Assessment of Fatigue questionnaire (MAF) and the Pittsburgh Sleep Quality Index (PSQI) on the day of their clinical assessment. Enzyme-linked immunosorbent assays (ELISA) were used to evaluate the serum concentrations of α-MSH, VIP and cytokines (IL-1β, IL-6, IL-10, and TNF-α). RESULTS: 64% of BD patients experienced high fatigue scores, and 63% had poor quality of sleep. When BD and HC were compared the MAF and PSQI scores as well as the serum concentrations of α-MSH, VIP, and IL-6 were significantly higher in BD (p values were: 0.001, 0.001, 0.001, 0.004 and 0.036, respectively). Both α-MSH and IL-6 had significant impact on MAF and PSQI. Interestingly, VIP had a significant influence on PSQI and disease activity, but not on MAF. CONCLUSIONS: A better understanding of these complex clinical and biochemical interactions between α-MSH, VIP and IL-6 might lead to the development of novel approaches to manage fatigue and sleep disorders as well as disease activity in BD patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".