Bibliographic record
Abstract
Dyslipidemia is a major contributor to cardiovascular morbidity and mortality. Although awareness of the importance of the risk of dyslipidemia has increased, treatment of dyslipidemia has not improved accordingly. Even though the actual number of individuals receiving treatment has increased, the proportion of those who are treated but did not reach the recommended treatment goal, is still disturbing. This problem is highlighted in this issue of Atherosclerosis by the article of S. Zhao et al. who in a cross-sectional study involving 25,697 Chinese individuals found that overall 38.5% of those receiving lipid-lowering treatment did not achieve the treatment goal for low density lipoprotein. Of particular concern is the authors' finding that the majority of these were individuals with a high cardiovascular risk and/or with type 2 diabetes mellitus. Some of the main reasons for this problem relate to patients' compliance with treatment and inertia on the side of physicians and patients to increase the dose of a given medication or move to a combination treatment. New medications with various and different pharmacological modes of actions and increased possibility for combination treatment may help to improve the treatment for dyslipidemia.
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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.025 | 0.027 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".