Abstract 243: Canadian Familial Hypercholesterolemia Registry
Bibliographic record
Abstract
Familial hypercholesterolemia (FH) is the most frequent genetic lipoprotein disorder associated with premature CAD. In Canada, the burden of disease is estimated to be approximately 83,500 patients. Objective: The goal of this initiative is to create a registry of subjects with FH across Canada. Rare diseases of lipoprotein metabolism will also be included (SMASH initiative). Methods and Results: Using a “hub and spoke” model, the registry will be extended in various communities to link primary care physicians with provincial academic centers. The registry will include clinical, biochemical and demographic information. Specimens (plasma/serum and DNA) will be collected for local biobanking. We propose a three-tier registry: local, provincial and Canada-wide, which will be completely anonymized. The registry will be made available for clinicians to manage patient care, identify relatives for screening and treatment (cascade screening), to provide advice to general practitioners and to support collaborative studies in biomedical, clinical, health outcomes and health economics research. The data extracted for the provincial portion of the database will allow administrative database research that will provide important information to key stakeholders and permit allocation of resources. It will also allow a sound and uniform rationale for the use of novel therapeutic agents and provide expert advice to regulatory agencies. At the Canadian level, the database will allow clinicians and researchers to determine the burden of disease and the long-term effects of treatment. Conclusion: Through the creation of a Canada-wide network of academic clinics, integrating lipid specialists, endocrinologists and cardiologists, the Canadian FH registry will lead to significant benefits for FH patients, clinicians and researchers, biopharmaceutical industry and government.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.038 | 0.010 |
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".