The Association of Serum Complement C1q with Coronary Artery Disease: A Systematic Review and Meta-Analysis.
Bibliographic record
Abstract
Background: Coronary artery disease (CAD) is one of the leading causes of death in middle-aged and elderly people, and its incidence has been increasing in recent years. An in-depth understanding of the pathogenesis of CAD is important to ensure the health of CAD patients. Objective: To analyze the association of serum complement C1q with CAD," you could say something like "The objective of this meta-analysis is to investigate the relationship between serum complement C1q levels and the presence of CAD, aiming to provide insights for clinical diagnosis and treatment. Methods: Relevant studies on C1q and CAD were searched in PubMed, Web of Science and other literature databases. Two research team members independently cross-screened the literature according to the inclusion-exclusion criteria and assessed the literature quality. RevMan5.3 software was used for statistical analysis. Results: Three references were finally included, all of which had a Newcastle-Ottawa Scale (NOS) score ≥6, indicating high quality. A total of 2065 subjects were studied, including 1249 in the experimental group (CAD patients) and 816 in the control group (healthy population). Through the meta-analysis, it was found that the experimental group (CAD patients) had higher serum C1q than the control group (healthy controls) (P < .05). According to subgroup analysis, age, sex, sample size, diabetes mellitus (with/without), and serum complement C1q detection methods were not factors affecting the heterogeneity of the literature, and more data are needed for verification. Validation analysis with the fixed-effect model also showed higher C1q expression in the experimental group (P < .05). The graph of the funnel plot was basically symmetrical, suggesting low publication bias. Conclusions: Serum complement C1q is elevated in CAD patients, but its mechanism of action may have a dual effect, but further research is needed to understand its precise role and clinical implications.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".