The correlation between stable angina and inflammatory factors and blood lipids: a case-control study
Bibliographic record
Abstract
Objective: In this study, we aimed to compare the levels of inflammatory markers (C-reactive protein, CRP; procalcitonin, PCT) and blood lipids (total cholesterol, TC; triglyceride, TG; high-density lipoprotein cholesterol, HDL-C; low-density lipoprotein cholesterol, LDL-C) between patients with stable angina and control group, and to explore the correlation between these parameters and the severity and prognosis of stable angina. Methods: We retrospectively selected 113 patients with stable angina and 128 control group from the medical record system, and compared their inflammatory factors and blood lipids. We also assessed the severity of angina using the Canadian Cardiovascular Society (CCS) classification and followed up the patients for 1 year to record any cardiovascular events. Results: We found that patients with stable angina had significantly higher levels of CRP, TC, TG, and LDL-C, and lower levels of HDL-C than control group. Moreover, CRP, TC, TG, and LDL-C were positively correlated with the severity of angina, while HDL-C was negatively correlated. During the follow-up period, 37 patients with stable angina experienced cardiovascular events, and they had higher levels of CRP, TC, TG, and LDL-C, and lower levels of HDL-C than those who did not. Conclusion: Our study suggests that inflammation and dyslipidemia are closely related to stable angina, and that inflammatory factors and blood lipids can be used as indicators of the severity and prognosis of stable angina.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".