Pattern of Macrovascular Complications and its relationship with HbA1c in Hospitalized Patients with Type 2 Diabetes Mellitus
Bibliographic record
Abstract
Background: Type 2 diabetes is a common metabolic disorder causing high blood sugar due to insulin resistance and relative insulin deficiency. It poses a significant global health burden, especially in low and middle-income countries. This study explores how well-controlled blood sugar (HbA1c) affects the types and patterns of complications in hospitalized patients with type 2 diabetes. Methods: Conducted over six months at a tertiary care hospital, this cross-sectional study included 200 men and women with T2DM. Data were collected through evaluations and documented. Macrovascular complications were identified using specific criteria: elevated cardiac troponin I for myocardial infarction, neurological deficits with imaging for stroke, and foot ulcers or amputation history for peripheral vascular disease. Results: Macrovascular complications were found in 33% of patients, with myocardial infarction being the most common (48.5%), followed by stroke (37.9%) and peripheral vascular disease (13.6%). Patients with complications had significantly higher HbA1c and blood glucose levels. Conclusion: A significant proportion of hospitalized T2DM patients have macrovascular complications, associated with higher HbA1c levels. These findings emphasize the importance of good glycemic control to prevent such complications. J MEDICINE 2024; 25: 136-140
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".