Implementation strategies for improving the care of familial hypercholesterolaemia from the International Atherosclerosis Society: next steps in implementation science and practice
Bibliographic record
Abstract
Familial hypercholesterolaemia (FH) is the most common monogenic condition associated with premature atherosclerotic cardiovascular disease. Early detection and initiation of cholesterol lowering therapy combined with lifestyle changes improves the prognosis of patients with FH significantly. The International Atherosclerosis Society (IAS) published a new guidance for implementing best practice in the care of FH. Previous guidelines and position statements seldom provided implementation recommendations. To address this, an implementation science approach was used to generate implementation strategies for the clinical recommendations made. This process entailed the generation by consensus of strong implementation recommendations according to the Expert Recommendations for Implementing Change (ERIC) taxonomy. A total of 80 general and specific implementation recommendations were generated, addressing detection (screening, diagnosis, genetic testing and counselling) and management (risk stratification, treatment of adults or children with heterozygous or homozygous FH, therapy during pregnancy and use of apheresis) of patients with FH. We describe here the IAS guidance core implementation strategies to assist with the adoption of clinical recommendations into routine practice for at-risk patients and families worldwide. We summarise the IAS guidance core implementation strategies as operative statements.
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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 | 0.277 | 0.346 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.015 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".