Low-Density Lipoprotein Cholesterol Targets in Patients With Coronary Heart Disease in Extremadura (Spain): LYNX Registry
Bibliographic record
Abstract
BACKGROUND: Low-density lipoprotein cholesterol (LDL-C) contributes decisively to the development of cardiovascular disease (CVD). In the LYNX registry we determined the rate of achievement of the target value of LDL-C, the use of lipid-lowering therapy (LLT) and the predictive factors of not reaching the target in patients with stable coronary heart disease (CHD). METHODS: LYNX included consecutive patients with stable CHD treated at the University Hospital of Caceres, Extremadura (Spain) from September 2016 to September 2018, and those who must have an LDL-C target below 70 mg/dL according to the European Society of Cardiology (ESC) 2016 guidelines. The variables independently associated with the breach of the LDL-C objective were evaluated by multivariable logistic regression. RESULTS: A total of 674 patients with stable CHD were included. The average LDL-C levels were 68.3 ± 24.5 mg/dL, with 56.7% showing a level below 70 mg/dL. LLT was used by 96.7% of patients, 71.7% were treated with high-powered statins and 30.1% with ezetimibe. The risk of not reaching the target value of LDL-C was higher in women, in active smokers, and in those who had multivessel CHD or had atrial fibrillation. Patients with diabetes mellitus, those who took potent statins or co-administration treatment with ezetimibe were more likely to reach the target level of LDL-C. CONCLUSIONS: The treatment of dyslipidemia in patients with chronic CHD remains suboptimal; however, an increasing number of very high-risk patients achieve the LDL-C objective, although there is still enormous potential to improve cardiovascular outcome through the use of more intensive LLT.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".