Prevalence of dyslipidaemia in patients treated with lipid-modifying drugs in the Netherlands. Part of the dyslipidaemia international survey.
Bibliographic record
Abstract
BACKGROUND: Patients at risk for cardiovascular disease require medical treatment to optimise their lipid profile. Failure to reach optimal lipid levels contributes significantly to the residual cardiovascular risk in treated patients. In the DYSIS -Netherlands study, residual lipid profile abnormalities in patients on stable statin therapy in the Netherlands were assessed. METHODS: As part of a multinational cross-sectional cohort study conducted in Canada and Europe, 1212 patients were included in the Netherlands. Patients aged 45 years and older were included if they had taken statins for at least three months. Data on demographics, cardiovascular history and cardiovascular risk profile were recorded, and compared using European Society of Cardiology (ES C) risk classification. RESULTS: In 1139 patients, total lipid profile was measured. In this population ESC LDL-cholesterol normal levels were not achieved in 33.3% (n=379), whereas 71.7% (n=817) did not reach the three-normal level: for LDL cholesterol, plus they had low HDL cholesterol and/or elevated triglycerides. In the high-risk group (n=1036), LDL-cholesterol levels were not at goal in 33.3% (n=345). In the entire cohort, only 28.3% (n=322) of patients receiving statin therapy reached normal levels for all lipid parameters. CONCLUSION: The majority of patients receiving statin therapy fail to reach normal levels for lipid parameters. Although the final results of ongoing outcome trials using combinations of lipid-altering treatments should be awaited, optimisation of lipid management is still amenable to improvement in the Netherlands.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".