COMPARISON OF FINGER-TOE PULSE WAVE VELOCITY (FTPWV) IN WOMEN WITH RHEUMATOID ARTHRITIS AND HEALTHY CONTROLS MEASURED WITH POPMÈTRE®
Bibliographic record
Abstract
Objective: The aim of this study was to describe the difference in finger-toe pulse wave velocity in patients with rheumatoid arthritis and healthy controls measured by pOpmètre®.Design and method: The pOpmètre is a new tool to evaluate arterial thickness as a way to prevent and approach cardiovascular disease. This was a cross-sectional and analytical study. We recruit women with RA according to the ACR 1987 criteria and healthy controls (> 18 years old and able to grant informed consent) without comorbidities. We made a complete medical and nutritional examination. Afterward, we determined arterial stiffness by finger-toe pulse wave velocity technique (ftPWV) with pOpmètre. We collected all data and calculated mean and standard deviation. Results: We recruited a total of 170 women, 81 RA patients and 89 healthy controls (HC). The data were analyzed using SPSS v.23. Continuous variables between groups were analyzed with t-student test. The normality of the data was evaluated with Kolmogorov-Smirnov. A two-tailed (p < 0.05) was considered statistically significant. No significant difference was observed in the rest of the measures. RA group showed worse parameters in weight and BMI than HC. Statistic differences of ftPWV were found in the 5th decade of life (P = 0.015) Figure 1. Conclusions: pOpmètre might be a useful tool for a screening of arterial stiffness in RA patients of 5th decade of life. pOpmètre is fast to apply and do not require a special training.
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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 | 0.002 |
| 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 | 0.000 |
| 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".