Management of patients with type 2 diabetes and cardiovascular disease in primary care
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Approximately 65% of cardiovascular disease (CVD)-related deaths in Australia occur in people with diabetes or pre-diabetes. The aim of this study was to investigate general practice management of risk factors among patients with both conditions. METHOD: This was a cross-sectional study of 33,559 adult patients with both type 2 diabetes and CVD at 1 November 2018, using the general practice data program MedicineInsight. RESULTS: One-third of patients did not have a record in their current medications list for all three recommended medicines to reduce cardiovascular risk. Potentially suboptimal monitoring and achievement of targets for diabetes and cardiovascular risk factors was also identified. Most patients using metformin-based combination therapy were prescribed blood glucose-lowering medicines that do not have evidence of cardiovascular benefit. DISCUSSION: These data suggest opportunities to support general practices to optimise patient management. Datasets such as MedicineInsight can help practices identify patients who may benefit from recall.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".