Life Course Approach for Managing Familial Hypercholesterolemia
Bibliographic record
Abstract
Treatment of familial hypercholesterolemia is directed toward the moment of the medical encounter. However, risk for heart disease as a consequence of having familial hypercholesterolemia is related to lifelong exposure to elevated low-density lipoprotein cholesterol, rather than low-density lipoprotein cholesterol level at a specific time point. The purpose of this review is to reassess contemporary research on treatment of familial hypercholesterolemia and current evidence-based guidelines, to present an approach that emphasizes treatment across the life course, and to recognize the importance of family experiences to care. To accomplish this, we review the changing treatment needs that emerge across the life course, from birth through childhood, adolescence, young adulthood, peripregnancy, middle age, and late in life. Special attention is paid to improving adherence to treatment, the potential role of monitoring atherosclerosis in a lifelong model of care, and medical issues related to care transitions: from pediatric to internal medicine care, peripregnancy, after a cardiac event, and care after age 70 years in the absence of a cardiac event. Novel considerations related to treatment of homozygous familial hypercholesterolemia are discussed. The summary identifies research gaps that need to be closed to move from the current point-of-care model to one that considers treatment over the life course.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".