Atherosclerosis in Rheumatoid Arthritis: What to Look for in Studies Using Carotid Ultrasound: Figure 1.
Bibliographic record
Abstract
Studies of myocardial infarction and other cardiovascular (CV) events in patients with rheumatoid arthritis (RA) have consistently found that their incidence is at least twice as high as in controls without RA1–6. However, there is still uncertainty about the cause and pathophysiology of CV disease in RA. One reason is that, in contrast to CV event findings, previous studies on the extent of atherosclerosis in RA have not consistently found it is increased in RA. In this issue of The Journal , Kobayashi and colleagues used carotid ultrasound to assess atherosclerosis in RA patients and controls7. In this commentary, I will first discuss the key technical aspects necessary for a high quality study using ultrasound; I will then briefly discuss published studies, focusing on those conducted in North American patient samples, and will end with a discussion of the article in this issue. High resolution carotid ultrasound has been used extensively to study atherosclerosis in the general population8. It provides a noninvasive, readily available method to image the major arteries of the neck. Its validity as a measure of atherosclerosis is supported by 3 lines of evidence: (1) the correlation between ultrasound and histological measurements of arterial intima-media thickness (IMT)9; (2) the correlation between histological measurements of carotid and coronary wall IMT10; and (3) the association between CV risk factors and ultrasound measurements of carotid IMT, and the ability of the latter to predict CV events in the general population8,11. A number of technical requirements must be present for the ultrasound results to be valid, as itemized below: ### Sample size Ultrasound uses ultra-high frequency sound waves that bounce, radar-like, off the body’s tissues to produce an image. Not surprisingly, this approach is limited by measurement error … Address correspondence to Dr. del Rincon; E-mail: delrincon{at}uthscsa.edu
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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 | 0.044 | 0.117 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.009 | 0.024 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.027 | 0.016 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".