The Effect Of Smoking On High-Density Lipoprotein (HDL), Low-Density Lipoprotein (LDL), And Triglycerides: A Review
Bibliographic record
Abstract
Smoking is a well-known risk factor for a range of cardiovascular diseases. One of the mechanisms through which smoking exerts its negative effects on the cardiovascular system is by altering lipid profiles, including levels of high-density lipoprotein (HDL), low-density lipoprotein (LDL), and triglycerides. This review aimed to investigate the effect of smoking on these lipid parameters based on current research evidence. A thorough search of relevant literature was conducted to gather data on the impact of smoking on HDL, LDL, and triglycerides. The results indicate that smoking is associated with decreased levels of HDL, increased levels of LDL, and elevated levels of triglycerides. These changes in lipid profiles can contribute to the development of atherosclerosis and cardiovascular events in smokers. The implications of these findings for public health and clinical practice are discussed, as well as potential limitations of the current research and directions for future studies.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| 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) | 0.004 | 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".