Association Between Coagulation Profiles and Platelet Count in Type 2 Diabetes Mellitus Patients: Insights from a Study in Nepal
Bibliographic record
Abstract
Type 2 Diabetes Mellitus (T2DM) presents a significant global health challenge, affecting metabolic processes and increasing cardiovascular risk.Elevated blood sugar levels in diabetes contribute to heightened clot formation and disrupt coagulation mechanisms, fostering atherosclerosis and altering platelet activity.This study aims to analyze the coagulation markers Prothrombin Time (PT), Partial Thromboplastin Time (PTT), and platelet counts in T2DM patients, investigating the influence of elevated blood sugar levels on coagulation changes.Conducted at the Nepal Cardio Diabetes and Thyroid Centre, this cross-sectional observational study selected T2DM-diagnosed patients as cases and healthy individuals as controls.Blood samples were analyzed using standard techniques, and statistical analysis was performed using the Statistical Package for the Social Sciences (SPSS) version 21.Significant differences were observed in PT, International Normalized Ratio (INR), PTT, and platelet counts between the cases and controls, indicating altered coagulation pathways and reduced platelet counts in T2DM patients.These findings suggest a hypercoagulable state in diabetic patients, contributing to atherogenesis.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".