A cross‐sectional study of the prevalence and clinical management of atherosclerotic cardiovascular diseases in patients with type 2 diabetes across the Middle East and Africa ( <scp>PACT‐MEA</scp> ): Study design and rationale
Bibliographic record
Abstract
AIM: To investigate the epidemiology and clinical management of patients with type 2 diabetes (T2D) and established atherosclerotic cardiovascular disease (eASCVD) or high/very high ASCVD risk, defined by the 2021 European Society of Cardiology Guidelines, in seven countries in the Middle East and Africa (PACT-MEA; NCT05317845), and to assess physicians' attitudes and the basis for their decision-making in the management of these patients. MATERIALS AND METHODS: PACT-MEA is a cross-sectional, observational study undertaken in Bahrain, Egypt, Jordan, Kuwait, Qatar, South Africa and the United Arab Emirates based on a medical chart review of approximately 3700 patients with T2D in primary and secondary care settings, and a survey of approximately 400 physicians treating patients with T2D. RESULTS: The primary and secondary objectives are to determine the prevalence of eASCVD and high/very high ASCVD risk in patients with T2D. Current treatment with cardioprotective antidiabetic medication, the proportion of patients meeting the treatment criteria for reimbursement in the study countries where there is an applicable reimbursement guideline, and physician-reported factors in clinical decision-making in T2D management, will also be assessed. CONCLUSIONS: This large cross-sectional study will establish the estimated prevalence and management of eASCVD and high/very high ASCVD risk in patients with type 2 diabetes across the Middle East and Africa.
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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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".