The design and rationale of SAVE BC: The Study to Avoid CardioVascular Events in British Columbia
Bibliographic record
Abstract
Atherosclerotic cardiovascular disease (ASCVD) is highly heritable, particularly when it occurs at a young age. The screening of individuals with premature ASCVD, although often recommended, is not routinely performed. Strategies to address this gap in care are essential. We designed the Study to Avoid CardioVascular Events in British Columbia (SAVE BC) as a prospective, observational study of individuals with a new diagnosis of very premature ASCVD (defined as age ≤ 50 years in males and age ≤ 55 years in females) and their first-degree relatives (FDRs) and spouses. FDRs and spouses will undergo screening for cardiovascular (CV) risk factors and subclinical ASCVD using a structured screening algorithm. All subjects will be followed longitudinally for ≥10 years. The overall goal of SAVE BC is to evaluate the yield of a structured screening program for identifying individuals at risk of premature ASCVD. The primary objectives of SAVE BC are to identify and follow index cases with very premature ASCVD and their FDRs and to determine the diagnostic yield of a structured screening program for these individuals. We will collect data on CV risk factors, medication use, CV events, and healthcare costs in these individuals. SAVE BC will provide insight regarding approaches to identify individuals at risk for premature ASCVD with implications for prevention and treatment in this population.
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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 | 0.032 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".