Low Serum Calcitonin Gene-Related Peptide Level is Associated with Severity of Coronary Stenosis
Bibliographic record
Abstract
PURPOSE: To evaluate the relationship between the serum calcitonin gene-related peptide (CGRP) level and severity of coronary stenosis. METHODS: A total of 233 eligible patients who underwent coronary angiography were divided into two groups: a control and a coronary heart disease (CHD) group. The angiographic severity of coronary stenosis was evaluated by SYNTAX and Gensini scores. The incidence of major adverse cardiovascular events within two years was collected. RESULTS: A negative correlation between serum CGRP levels and Gensini scores was observed in all patients (r=-0.352, p<0.001), the control group (r=-0.422, p<0.001) and the CHD group (r=-0.393, p<0.001). Serum CGRP levels were negatively associated with SYNTAX scores in the CHD group (r=-0.522, p<0.001). The area under the curve of CGRP for identifying high SYNTAX scores (>22) was 0.772 [95% confidence interval (CI): 0.673-0.870, p<0.001], and for identifying high Gensini scores was 0.744 (95% CI: 0.646-0.842, p<0.001). A CGRP concentration of 25.05 pg/ml was selected as the cutoff point. A low CGRP level (<25.05 pg/ml) was an independent predictor of severe coronary stenosis, a SYNTAX score >22 [odds ratio (OR) =5.819, 95% CI: 2.240-15.116; p<0.001] and a high Gensini score (>64) (OR=4.943, 95% CI: 2.020-12.095; p<0.001). The low CGRP group had a higher incidence of major adverse cardiovascular events within two years (11.1 vs. 3.1%, p=0.031). CONCLUSION: In coronary atherosclerosis patients without acute myocardial injury, serum CGRP levels were negatively associated with the severity of coronary stenosis.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".