Bibliographic record
Abstract
Current Opinion in Lipidology was launched in 1990. It is part of a successful series of review journals whose unique format is designed to provide a systematic and critical assessment of the literature as presented in the many primary journals. The field of lipidology is divided into six sections that are reviewed once a year. Each section is assigned a Section Editor, a leading authority in the area, who identifies the most important topics at that time. Here we are pleased to introduce the Section Editor for this issue. SECTION EDITOR Robert HegeleRobert HegeleRob Hegele is a distinguished University Professor of Medicine and Biochemistry, University of Western Ontario, Canada, and Director of the Lipid Genetics Clinic and the London Regional Genomics Centre in London, Ontario, Canada. Professor Hegele cares for more than 2,800 patients in his lipid clinic at University Hospital in London. His laboratory discovered the causal genes for more than 20 human diseases and also developed the world's first targeted next-generation sequencing panel for dyslipidemias. He is also known for characterizing the polygenic basis of human lipid disorders. He was among the first in the world to use five medications that are now routinely prescribed to treat dyslipidemia or diabetes. He has published more than 960 papers with over 88,000 citations on Google Scholar and is in the top 1% of highly cited scientists in the world. The website Expertscape.com in 2024 ranked him #1 globally in the area of “hypertriglyceridemia” and #2 for “disorders of lipid metabolism”. He received the 2019 American Heart Association Lyman Duff Award, the 2020 FH Foundation Pioneer Award and 2024 US National Lipid Association Virgil Brown Distinguished Achievement Award. He has co-authored many clinical practice guidelines for cholesterol, blood pressure and diabetes. He has trained numerous physicians, medical students and graduate students.
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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.210 | 0.114 |
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".