Correlation between platelet metrics and cardiovascular risk in prediabetes with coronary artery disease: A two-year cross-sectional study
Bibliographic record
Abstract
Prediabetes is associated with coronary artery disease (CAD), as even a 1% increase in glycated hemoglobin (HbA1c) may increase CAD severity and associated mortality over ten years. A definite association exists between platelet indices, CAD, and diabetes mellitus. Although research has demonstrated an association between CAD and prediabetes as well as platelet indices, there have been no attempts to assess the association of platelet indices in prediabetic patients who are at risk of CAD. A cross-sectional study took place between 2019 and 2020 in the medical department of a rural medical college located in Central Maharashtra, India. A total of 180 patients with prediabetes and documented CAD on coronary angiography were enrolled in this study. For all participants, platelet indices, blood sugar levels, glycosylated hemoglobin (HbA1c) levels, lipid profiles, and anthropometric measurements were recorded, and then statistical analysis was conducted. Mean platelet volume had a substantial positive correlation with HbA1c, fasting blood sugar, postprandial blood sugar, systolic blood pressure, diastolic blood pressure, body mass index, waist circumference, and waist/hip ratio, with correlation coefficients of 0.2, 0.173, 0.219, 0.218, 0.234, 0.165, 0.182, and 0.164, respectively. A significant negative correlation was found between platelet distribution width and high-density lipoprotein (HDL) level, with a correlation coefficient of −0.373. Platelet indices, which are routinely available through standard clinical investigations, can effectively predict the risk of CAD in prediabetic patients. Their strong association with multiple risk factors allows for enhanced prognosis and facilitates early intervention planning for CAD in this high-risk group.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".