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 A. HegeleRobert A. HegeleRobert A. Hegele is 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. He received his MD degree from the University of Toronto, Canada in 1981. His specialty training in internal medicine and in endocrinology & metabolism was also in Toronto. His post-doctoral research fellowships were at Rockefeller University, USA, and Howard Hughes Medical Institute, University of Utah, USA. From 1989 to 1997 he was on the Faculty of Medicine at the University of Toronto. In 1997 Dr Hegele joined the Schulich School of Medicine and Dentistry and the Robarts Research Institute at the Western University, in London, Ontario, Canada, where he holds the Jacob J. Wolfe Distinguished Medical Research and the Martha Blackburn Chair in Cardiovascular Research. His lab studies the genetics of lipoprotein metabolism, cardiovascular disease and diabetes mellitus. Solely or through collaborations, his lab was first to describe the molecular genetic basis of 14 human diseases. His lab has also defined much of the genetic basis of such complex traits, including hypertriglyceridemia. He has (co-) authored over 510 peer-reviewed publications and has contributed to national treatment guidelines for dyslipidemia, hypertension and diabetes. He is a member of the American Society of Clinical Investigation and the Canadian Academy of Health Sciences, among several other professional organizations. He serves on editorial boards of the Journal of Lipid Research and several American Heart Association journals. He has trained numerous physicians 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 | 0.033 |
| 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 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.174 | 0.092 |
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".