Lipids and Diabetes
Bibliographic record
Abstract
Abstract Diabetes is now a major health issue worldwide, affecting millions of people. Diabetes negatively impacts the quality of life and productivity of subjects in both direct and indirect ways. A range of abnormalities are associated with diabetes. One of such abnormalities is alterations in lipid metabolism, resulting in the production of atherogenic lipoprotein profile. Subjects with long‐term diabetes usually develop a state of dyslipidemia known as “diabetic dyslipidemia.” These subjects usually have higher serum triacylglycerol levels with increased levels of small dense low‐density lipoprotein as well as lower levels of high‐density lipoprotein cholesterol. Many strategies have been suggested for the management of diabetes and associated dyslipidemia. Among them, changes in dietary habits and lifestyle modification are included in the first line of management strategies. Many such patients may require lipid‐lowering drugs. In this regard, several countries have developed guidelines for the management of “diabetic dyslipidemia.” In this article features of “diabetic dyslipidemia” as well as its management strategies have been summarized.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.010 |
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".