Bibliographic record
Abstract
already know — many Canadians are still confused about cho-lesterol, dietary sources of fat and how these substances affect their lipid levels.1 Despite years of disseminating health promo-tion messages and statistics on the incidence of cardiovascular disease, the Heart and Stroke Foundation of Canada reports that nearly 10 million adult Canadians still have cholesterol levels that exceed the recommendations for their risk profile.2 Dr. Glen Pearson, our guest editor for this supplement, and our group of expert contributors join me in encouraging all pharmacists to use the information and tools in this supple-ment to better educate their patients. Pharmacists can improve the management of dyslipidemia and should take an active role in preventing cardiovascular disease. ■ Rosemary M. Killeen is the Editor-in-Chief of the Canadian Pharmacists Journal and a part-time community pharmacist. Contact:
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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.017 | 0.024 |
| Insufficient payload (model declined to judge) | 0.055 | 0.015 |
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".