Bibliographic record
Abstract
Dyslipidemia is highly prevalent among women. The management of dyslipidemia is a cornerstone in the prevention of both primary and secondary cardiovascular events, such as myocardial infarction, ischemic stroke, and coronary death. All major international guidelines on the treatment of dyslipidemia recommend similar approaches to the management of dyslipidemia in both men and women. Estrogen replacement therapy should not be considered as a therapeutic option for managing dyslipidemia in women. The reduction of atherogenic lipoprotein burden (reducing low-density lipoprotein cholesterol and non-high-density lipoprotein cholesterol based on risk-stratified thresholds and treatment targets) provided the framework for managing dyslipidemia in the US, Europe, Canada, and elsewhere in the world. Very recently, new guidelines in the US have changed this paradigm, whereby rather than focusing on treatment targets, risk now defines the intensity of treatment with statin therapy, with no specific goals for what level of low-density lipoprotein cholesterol should be attained. It is not clear if this will lead to changes in lipid guidelines in other parts of the world. In the meantime, region-specific guidelines should be followed. Lipid lowering with statin therapy does correlate with reductions in cardiovascular event rates in women. The clinical impact of treating dyslipidemias in women with nonstatin drugs (eg, fibrates, nicotinic acid, bile acid-binding resins, omega-3 fish oils) is as yet not determined.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 0.001 |
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".