Glycated hemoglobin predicts coronary artery disease in non-diabetic adults
Bibliographic record
Abstract
BACKGROUND: Coronary artery disease (CAD) is a major cause of morbidity and mortality worldwide. Due to increased CAD risk factors in Saudi Arabia, research on more feasible and predictive biomarkers is needed. We aimed to evaluate glycated hemoglobin (HbA1c) as a predictor of CAD in low-risk profile non-diabetic patients living in the Al Qassim region of Saudi Arabia. METHODS: Thirty-eight patients with no history of CAD were enrolled in this cross-sectional study. They provided demographic data, and their HbA1c estimation followed the National Glycohemoglobin Standardization Program parameters. All patients underwent coronary computed tomography angiography (CCTA) for evaluation of chest pain. The extent of coronary artery stenosis (CAS) was quantified as percentage for each patient based on plaques detected in CCTA. RESULTS: ), serum cholesterol level (174 ± 33.1 mg/dl), and HbA1c levels (mean 5.7 ± 0.45, median 5.7 and range 4.7-6.4%). Eighteen patients showed no CAS (47.4%), 12 showed minimal stenosis (31.6%), 3 showed mild stenosis (7.9%), 3 showed moderate stenosis (7.9%) and 2 showed severe stenosis (5.3%). A moderate correlation was detected between HbA1c and CAS percentages (r = 0.47, p < 0.05) as well as between HbA1c and the number of affected coronary vessels (r = 0.53, p < 0.001). CONCLUSION: Glycated hemoglobin can be used as a predictive biomarker for CAD in non-diabetic low-risk patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".