Prevalence and Risk Factors for Acute Coronary Syndrome Among Sudanese Individuals With Diabetes: A Population-Based Study
Bibliographic record
Abstract
BACKGROUND: Acute coronary syndrome (ACS) is common with diabetes. The aim of this study was to establish prevalence and risk factors for ACS among Sudanese individuals with diabetes mellitus (DM). METHODS: A descriptive cross-sectional study was conducted in Khartoum State and Atabra (North of Sudan). The data collection was performed through pretested questionnaire, in addition to measurement of lipid profile and HbA1c. RESULTS: Among the 496 respondents, 234 (48.4%) were males and only 15 of them had ACS, while 255 (51.6%) were females and 12 had ACS. Therefore, the prevalence of ACS was 5.44% (95% confidence interval (CI): 3.45-7.44%). The unadjusted risk factors for ACS were age (P = 0.0008864), duration of DM (P = 0.01105) and presence of hypertension (P = 0.0006021). The presence of albuminuria, gender, and body mass index (BMI) was not associated with ACS. In addition, parameters like HbA1c, high-density lipoprotein (HDL), triglyceride and low-density cholesterol (LDL) were also not associated with ACS. Logistic regression analysis showed that duration of diabetes (odds ratio (OR) for below 5 years' history of DM = 0.175 (95% CI for OR: 0.180 - 0.835), P = 0.04051) and hypertension (OR = 2.462 (95% CI for OR: 1.007 - 6.500), P = 0.039) were absolute risk factors for ACS. CONCLUSION: The prevalence of ACS is estimated to be 5.44%. ACS in Sudanese individuals with DM was associated with hypertension, increase in age and increase in duration of DM.
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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 | 0.001 | 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 | 0.000 | 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".