The count of tender rather than swollen joints correlates with aortic stiffness in patients with rheumatoid arthritis
Bibliographic record
Abstract
BACKGROUND: Patients with rheumatoid arthritis (RA) are at a higher cardiovascular (CV) risk in comparison to the general population. CV risk associates closely with aortic stiffness. Aim of this exploration was therefore to evaluate aortic stiffness in patients with RA and to examine its association with various RA associated parameters as well as with traditional CV risk factors. METHODS: Measurements of carotid-femoral pulse wave velocity (cfPWV) were analyzed retrospectively in 38 RA patients and 25 controls. We investigated the statistical difference between cfPWV values in the two groups. Furthermore, we analyzed the associations of cfPWV with laboratory and clinical RA parameters including Disease Activity Score 28 and its components, rheumatoid factor, cyclic citrullinated peptide antibodies, antinuclear antibodies and RA duration. Finally, we explored the relationship of cfPWV with traditional CV risk factors in the RA group. RESULTS: cfPWV was not significantly higher in RA patients in comparison to controls in an adjusted statistical model for confounding factors [-0.587 95 % CI (-1.38 to 0.201), p = 0.144]. Among RA patients there was a statistically significant correlation of cfPWV with age (rho = 0.544, p = 0.001) and the count of tender joints [0.051 95 % CI (0.008-0.207), p = 0.034]. Finally, C-reactive protein associated only marginally with cfPWV [0.105 95 % CI (-0.410 to 0.003), p = 0.053]. CONCLUSIONS: In RA patients the number of tender, rather than swollen joints correlates with stiffness of the aorta, as measured through cfPWV. Therefore, RA associated joint pain might play a role in the development of aortic stiffness and thus increase CV risk.
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 | 0.001 | 0.003 |
| 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 | 0.001 |
| 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".