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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".