AUDIT study. Evidence of global undertreatment of dyslipidaemia in patients with type 2 diabetes mellitus
Bibliographic record
Abstract
The Analysis and Understanding of Diabetes and Dyslipidaemia: Improving Treatment (AUDIT) study was a confidential, web-based, cross-sectional survey involving 2,043 diabetes specialists in 50 countries. The study investigated the attitudes of physicians specialising in the treatment of patients with type 2 diabetes mellitus towards the management of dyslipidaemia and other cardiovascular risk factors in these patients. Physicians reported obtaining lipid profiles in 91% of patients with type 2 diabetes and estimated that 62% of type 2 diabetic patients have dyslipidaemia. Across all regions, stated low-density lipoprotein cholesterol (LDL-C), triglyceride and total cholesterol targets were lower for type 2 diabetic patients with than without cardiovascular disease (CVD). Fewer physicians reported having an LDL-C target of < 2.6 mmol/L (≤100 mg/dL) for patients without CVD (59%) than with CVD (85%). Physicians reported that 54% of patients achieve LDL-C targets, with significantly more estimated to achieve their LDL-C goal in North America (69%) than in any other region (43—61%; p<0.001). When setting targets, 58% of physicians stated that they were most influenced by lipid management guidelines, although a large proportion of physicians from Eastern Europe (54%) and Africa/Middle East (50%) cited a personal read of the literature. Patient compliance was the most commonly perceived barrier to lipid goal attainment in most regions (42—61%); financial constraints were cited most often in South America (76%), Africa/Middle East (65%) and Eastern Europe (63%). The AUDIT study revealed a disparity between lipid screening and control in type 2 diabetic patients. Physicians reported that they treated patients without CVD less intensively than patients with CVD, suggesting that type 2 diabetes was not widely considered a coronary heart disease risk equivalent. A reassessment of guideline implementation is needed for physicians worldwide to improve lipid control to decrease cardiovascular risk in type 2 diabetes.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".