A qualitative systematic review of the prevalence of coronary artery disease in systemic sclerosis
Bibliographic record
Abstract
AIMS: To review existing literature on the prevalence/incidence of coronary artery disease (CAD), and secondarily highlight risk factors for CAD in systemic sclerosis (SSc). METHODS: A PubMed and Cochrane Central Register of Controlled Trials search of studies (till 30 November 2013) relating to SSc and CAD was performed, retrieving 180 titles. INCLUSION CRITERIA: studies reporting CAD prevalence/incidence in SSc based on autopsy findings, coronary artery calcium scores, coronary angiographic findings and physician/patient-reported CAD. EXCLUSION CRITERIA: (i) not written in English; (ii) not concerned with human subjects; (iii) single case reports or review articles; (iv) genetic studies; and 95) other surrogate outcome measures of atherosclerosis. Quality assessment was done using the Newcastle-Ottawa score (range 0-9). RESULTS: Thirteen studies (Newcastle-Ottawa score 5-8) were selected. Of eight studies with controls, seven reported increased CAD prevalence (10-56%) or incidence (2.3%) compared to controls (prevalence 2-44%; incidence 1.5%). Of five studies without controls, CAD prevalence was 8-32%. Five of six studies reported that traditional cardiovascular risk factors were similar/reduced in SSc compared to controls. SSc was an independent risk factor for CAD, in addition to age (n = 2), hypercholesterolaemia (n = 3), male gender (n = 1), hypertension and diabetes (n = 1). Disease duration, renal involvement and pulmonary arterial hypertension were associated with CAD. CONCLUSIONS: Systemic sclerosis is associated with an increased prevalence/incidence of CAD. SSc is an independent risk factor for CAD. The association of CAD with SSc-related factors requires further research. Meanwhile, patients with SSc should be screened and treated for identified traditional cardiovascular risk factors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".