Bibliographic record
Abstract
Bridging the gap between new evidence and practice is an ongoing struggle for physicians. To help them stay on top of things, a team from the Ottawa Cardiovascular Centre (www.ottawacvcentre.com) has created an electronic resource to provide clinicians with accessible, up-to-date distillations of the latest cardiovascular research, along with patient information and evidence-implementation tools. Dr. Joel Niznick, deputy chief of cardiology at the Riverside Site of the Ottawa Hospital and a managing partner at the Ottawa Cardiovascular Centre, is principal author of the Cardiovascular Toolbox (www.cvtoolbox.com). He says there is no shortage of data and guidelines for reducing cardiovascular mortality. “Unfortunately, the evidence shows that we are far from accomplishing optimal therapy. There is a huge gap between what we know and what we do. I'm trying to bridge this gap using these online tools.” He developed the Toolbox from his own collection of working documents and resources, which he and his colleagues have been using in their practices. The site, which is supported by unrestricted education grants from several pharmaceutical companies, provides access to a wide variety of tools and information aimed at physicians and patients. The content ranges from diabetes-management resources to a section on cholesterol and a CV-risk checklist that forms the core of what Niznick calls a “virtual cardiac prevention clinic.” “For a long time we've been focused on having experts produce appropriate and applicable guidelines, but that's not enough. We need to make them accessible, and that's what we're trying to with this site.” — Michael OReilly, ten.ylliero@ekim
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.206 | 0.050 |
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".