Effect of TNF alpha inhibitors on atherosclerosis in psoriatic arthritis patients: a systematic review protocol
Bibliographic record
Abstract
INTRODUCTION: Psoriatic arthritis (PsA) is a chronic inflammatory condition associated with joint and skin involvement. Cardiovascular disease, particularly atherosclerosis, is a leading cause of morbidity and premature mortality in PsA patients, due to persistent systemic inflammation. Tumor necrosis factor-alpha (TNF-α) inhibitors have proven effective in reducing inflammation in PsA, and some evidence suggests they may also improve vascular health. However, their specific impact on atherosclerosis in PsA remains unclear. OBJECTIVE: This systematic review aims to evaluate the effect of TNF-α inhibitors on the progression of atherosclerosis in adults with PsA. METHODS: This review will follow the PRISMA guidelines. Systematic searches will be conducted in PubMed, Scopus, Embase, and the Cochrane Library using validated search strategies. We will include original observational studies and randomized controlled trials published in English that assess PsA patients treated with TNF-α inhibitors, with comparison groups receiving conventional DMARDs, placebo, or pre-treatment baselines. Studies focusing on direct measures of atherosclerosis or endothelial dysfunction were selected. Reviews, editorials, case reports, and non-English publications will be excluded. Two independent reviewers will conduct the study selection, data extraction, and quality assessment using the Newcastle-Ottawa Scale for observational studies and the RoB 2 tool for RCTs. Only high-quality studies will be included. A narrative synthesis will be employed due to expected heterogeneity in study designs and outcomes.
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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 | 0.045 | 0.038 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.019 | 0.017 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.064 | 0.006 |
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".