Targeting the vivid facets of apolipoproteins as a cardiovascular risk factor in rheumatoid arthritis
Bibliographic record
Abstract
Mostly, cardiovascular diseases are blamed for casualties in rheumatoid arthritis (RA) patients. Customarily, dyslipidemia is probably the most prevalent underlying cause of untimely demise in people suffering from RA as it hastens the expansion of atherosclerosis. The engagement of inflammatory cytokines like tumor necrosis factor-α (TNF-α), interleukin-1 (IL-1), interleukin-6 (IL-6), etc., is crucial in the progression and proliferation of both RA and abnormal lipid parameters. Thus, lipid abnormalities should be monitored frequently in patients with both primary and advanced RA stages. An advanced lipid profile examination, i.e., direct role of apolipoproteins associated with various lipid molecules is a more dependable approach for better understanding of the disease and selecting suitable therapeutic targets. Therefore, studying their apolipoproteins is more relevant than assessing RA patients' altered lipid profile levels. Among the various apolipoprotein classes, Apo A1 and Apo B are primarily being focused. In addition, it also addresses how calculating Apo B:Apo A1 ratio can aid in analyzing the disease's risk. The marketed therapies available to control lipid abnormalities are associated with many other risk factors. Hence, directly targeting Apo A1 and Apo B would provide a better and safer option.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".