The Relationship between Cardiovascular Disease and Inflammatory Arthritis, and the Potential Role of Exercise on the Burden of Illness: a Scoping Review
Bibliographic record
Abstract
Inflammatory arthritis has been identified as a source of increased risk of cardiovascular disease (CVD) and patients with inflammatory arthritis face higher mortality compared to the general population. Hence, patients with inflammatory arthritis may require management of CVD that is different from the general population. Exercise has been described as a primary strategy for CVD risk management in the general population. However, it is unclear whether the presence of inflammatory arthritis and CVD risk factors requires different approaches to CVD risk management and in which aspects risk is changed (clinical or physiological). We conducted a scoping review of the literature to determine the association between inflammatory arthritis and CVD risk factors including prevention and treatment; specifically exercise treatment. Medical literature extracted using the databases SPORTDiscus, SCOPUS, and PUBMED, to determine the effect of exercise on the relationship between inflammatory arthritis and CVD was sought. We found 64 articles with relevant abstracts of studies and followed that by independent review by 2 reviewers. The majority of studies were excluded if the abstracts addressed an irrelevant study population, addressed only one of cardiovascular disease or inflammatory arthritis in isolation, did not provide a study design, or were lacking a complete abstract.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".