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 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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.027 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".