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 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 | 0.051 | 0.135 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.023 | 0.018 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".