Changes in biomarkers of lipid in juvenile idiopathic arthritis and its association with various disease parameters: a 6-month follow-up study
Bibliographic record
Abstract
Background: The objectives of our study were to determine the changes in lipid profile (total cholesterol (TC), triglyceride (TG), high density lipoprotein cholesterol (HDL-C), low density lipoprotein cholesterol (LDL-C)) in juvenile idiopathic arthritis (JIA) and assess its association with epidemiological and clinical profile of JIA patients. Methods: This observational study was performed with 46 patients at presentation followed by 38 cases at 3 months and 18 cases at 6 months of follow up. Their demographic profile and clinical parameters including juvenile disease activity score (JADAS 27) were compared with the biomarkers of lipid profile. Results: The mean (SD) age was 105.85 (20.23) months at first visit with mean (SD) disease duration being 15 (6.4) months. Twenty-six participants had oligoarthritic (56.5%), while the rest had polyarthritis (43.4%). Most of the patients had borderline raised TG and LDL-C (cases with raised TG n=14 (30.4%), 12 (31.5%), 5 (27.7%) at 1st visit, 3 months, and 6 months respectively and LDL-C n=12 (26%), 10 (26.3%), 6 (33.3%) at 1stvisit, 3 months, and 6 months respectively). HDL-C level was low in 36 (78.2%) cases at first visit, 28 cases (73.6%) at 3 months and 12 cases (66.6%) at 6 months respectively. Lipid profile was significantly affected by gender difference, duration of disease and drug therapy (p<0.05). Significant association have been found between JADAS score and TGL level with p value 0.03. Conclusions: Children with JIA definitely suffer from dyslipidemia. Among the biomarkers of lipid profile, low level of HDL-C is one of the most important highlights of our study. Further studies can help to strengthen the findings and formulate necessary interventions at an early stage.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".