LIPID PROFILE ALTERATION IN RHEUMATOID ARTHRITIS PATIENTS
Bibliographic record
Abstract
INTRODUCTION: It has been proved that rheumatoid arthritis (RA) is linked to dyslipidemia and the risk of cardiovascular complications is higher in these patients. The aim of this study was to evaluate dyslipidemia in RApatients. MATERIAL AND METHODS: In this study, RA patients were enrolled regarding the inclusion and exclusion criteria. Their demographic information and medication proles were evaluated. Clinical assessments were performed by evaluation of disease activity score (DAS28) and visual analogue scale. Moreover, laboratory investigations of lipid prole including triglycerides (TG), total cholesterol (Chol), low-density lipoprotein (LDL), and high- density lipoprotein (HDL) were performed. RESULT:Total of 150 patients with the mean age of 53.8±15.7 years, 64.4% were diagnosed with dyslipidemia. Females in menopausal ages had a higher prevalence of dyslipidemia as well as patients with longer disease duration. Considering DAS28, 100% of the patients with high disease activity were diagnosed with dyslipidemia. CONCLUSION:According to the results, patients under treatment with prednisolone and methotrexate were more affected by dyslipidemia than those with prednisolone, methotrexate, and hydroxychloroquine. In the patients under prednisolone, methotrexate, and leunomide treatment, the prevalence of dyslipidemia was signicantly lower than those used only prednisolone and methotrexate. Altogether, it is necessary to have more clinical suspicion towards dyslipidemia and its complications in the patients with a greater number of affecting factors.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".